Non-additive genetic effects induce novel phenotypic distributions in male mating traits of F1 hybrids

Supplementary Information

Setup

Load packages

rm(list=ls())  # reset workspace
options(scipen=100)  # do not show numbers using exponential

# install & load packages
pacman::p_load(
  openxlsx   # open excel
  , tidyverse
  , magrittr   # extended pipe
  , foreach    # Parallel processing
  , metafor    # meta-analysis
  , MCMCglmm   # comparative analysis
  , orchaRd    # Orchard plot - meta-analysis
  , rotl       # open tree of life
  , ape        # Phylogeny
  , phytools   # Phylogeny
  , knitr  
  , kableExtra # nice tables
  , pander     # nice tables
  , svglite    # Export SVG plots
  , ggiraphExtra # Geom predict
  , ggbeeswarm # Orchard plot
  , ggtree
  , patchwork   # combine multiple plots
  # , R.rsp
)

# Rmarkdown settings
knitr::opts_chunk$set(
  prompt  = FALSE,  # Do not add > or + in inline-code
  message = FALSE, 
  comment = "", 
  warning = FALSE,  # Mute warnings
  tidy    = TRUE
  ) 
options(knitr.kable.NA = '') # Hide NAs in kable table

Custom function

We have custom functions named : get_pred(), get_reg(), get_fixed.MCMCglmm() and get_random.MCMCglmm(), all of which are used later (see below for their functionality) and the code are included here.

#' Title: get_pred
#' function to get prediction intervals (crediblity intervals) from rma objects (metafor)
#'
#' @param model: rma.mv object 
#' @param mod: the name of a moderator 
get_pred <- function(model, mod = " ") {
  I2 <- i2_ml(model) %>%
    as.data.frame() %>%
    rownames_to_column("Partition") %>%
    rename("I2" = ".") %>%
    mutate(percentage = str_c(round(I2*100,1),"%", sep = "")) %>%
    column_to_rownames("Partition")

  name <- as.factor(str_replace(row.names(model$beta), mod, ""))
  len <- length(name)
  
  if (len != 1) {
      newdata <- matrix(NA, ncol = len, nrow = len)
      for (i in 1:len) {
          # getting the position of unique case from X (design matrix)
          pos <- which(model$X[, i] == 1)[[1]]
          newdata[, i] <- model$X[pos, ]
      }
      pred <- predict.rma(model, newmods = newdata)
    } else {
        pred <- predict.rma(model)
    }

    table <- tibble(
      'Dataset' = "",
      'Fixed effects' = name, 
      'Estimate' = model$beta, # regression: estimate
      'LowerCI' = model$ci.lb, # regression: 95ci, 
      'UpperCI'  = model$ci.ub, # regression: 95ci
      'lowerPR' = pred$cr.lb,  # lower prediction range
      'upperPR' = pred$cr.ub, # lower prediction range
      'P'        = model$pval,
    # 'V[species with phylogeny]' =
    #   c(model$sigma2[1], rep("", length(model$beta))),
    # 'V[study]' =
    #   c(model$sigma2[2], rep("", length(model$beta))),
    # 'V[crossed strain]' =
    #   c(model$sigma2[3], rep("", length(model$beta))),
    # 'V[residual]' = 
    #   c(model$sigma2[4], rep("", length(model$beta))),
    'I2[total]' = 
      c(I2["I2_total","percentage"], rep("", length(model$beta)-1)),
    '%I2[species with phylogeny]' = 
      c(I2["I2_spL.name","percentage"], rep("", length(model$beta)-1)),
    '%I2[study]' =
      c(I2["I2_Study.ID","percentage"], rep("", length(model$beta)-1)),
    '%I2[crossed strain]' =
      c(I2["I2_Cross.ID","percentage"], rep("", length(model$beta)-1)),
    '%I2[residual]' = 
      c(I2["I2_ES.ID","percentage"], rep("", length(model$beta)-1)),
    )  %>%
    as.data.frame() %>%
    mutate_at(vars('Estimate':'P'), as.numeric) %>%
    mutate(significance = ifelse(P < 0.05, "*", ""))
}



#' Title: get_reg
#' function to get summary stats from rma objects (metafor)
#' @param model: rma.mv object 
#' @param mod: the name of a moderator 
get_reg <- function(model, mod = " ") {
  
  I2 <- i2_ml(model) %>%
    as.data.frame() %>%
    rownames_to_column("Partition") %>%
    rename("I2" = ".") %>%
    mutate(percentage = str_c(round(I2*100,1),"%", sep = "")) %>%
    column_to_rownames("Partition")
  
  table <- tibble(
    'Dataset' = "",
    'Fixed effects' = c("", row.names(model$beta)),
    'Estimate' = c("", model$beta), # regression: estimate
    'LowerCI' = c("", model$ci.lb), # regression: 95ci, 
    'UpperCI'  = c("", model$ci.ub), # regression: 95ci
    'P'        = c("", model$pval),
    # 'V[species with phylogeny]' =
    #   c(model$sigma2[1], rep("", length(model$beta))),
    # 'V[study]' =
    #   c(model$sigma2[2], rep("", length(model$beta))),
    # 'V[crossed strain]' =
    #   c(model$sigma2[3], rep("", length(model$beta))),
    # 'V[residual]' = 
    #   c(model$sigma2[4], rep("", length(model$beta))),
    'I2[total]' = 
      c(I2["I2_total","percentage"], rep("", length(model$beta))),
    '%I2[species with phylogeny]' = 
      c(I2["I2_spL.name","percentage"], rep("", length(model$beta))),
    '%I2[study]' =
      c(I2["I2_Study.ID","percentage"], rep("", length(model$beta))),
    '%I2[crossed strain]' =
      c(I2["I2_Cross.ID","percentage"], rep("", length(model$beta))),
    '%I2[residual]' = 
      c(I2["I2_ES.ID","percentage"], rep("", length(model$beta))),
    )  %>%
    as.data.frame() %>%
    mutate_at(vars('Estimate':'P'), as.numeric) %>%
    mutate(significance = ifelse(P < 0.05, "*", ""))
}


#' Title: get_random.MCMCglmm
#' 
#' @param x: VCV of MCMCglmm products (i.e. summary('MCMCglmm object'$VCV)), which was transformed if apprecable. 
get_random.MCMCglmm <- function(x) {
  
  bind_cols(
    x$statistics %>%
    as.data.frame,
    x$quantiles %>%
    as.data.frame
    )  %>% 
    rownames_to_column("Random effects")  %>% 
    rename("Estimates" = "Mean") %>%
    as.data.frame %>%
    mutate(
      '95% credible interval' = str_c(
        round(.[,"2.5%"],2), round(.[,"97.5%"],2),
        sep = "--" # en-dash
        )
      ) %>%
    dplyr::select("Random effects", "Estimates", "95% credible interval") 
  
}

#' Title: get_fixed.MCMCglmm
#' 
#' @param x: Solution of MCMCglmm products (i.e. summary('MCMCglmm object'$solutions)), which was transformed if apprecable. 
get_fixed.MCMCglmm <- function(x) {
  
  x %>%
    as.data.frame() %>%
    rownames_to_column(var = "Factors") %>%
    mutate(
      '95% credible interval' = str_c(
        round(.[,"l-95% CI"],2), round(.[,"u-95% CI"],2),
        sep = " -- " # en-dash
        ),
      Estimates = round(post.mean, 2),
      P = round(pMCMC, 3)
      ) %>%
    mutate(significance = ifelse(P < 0.05, "*", "")) %>%
    mutate(
      "Description" = 
        str_c("$\\beta$ = ", Estimates,
              ", CI = ", .[, "95% credible interval"],
              ", P = ", round(P, 3),
              "")
      )

}
  
#++++++++++++++++++++++++++++++++++++++++++++++#
# Setting plot theme for forest plots
#++++++++++++++++++++++++++++++++++++++++++++++#
foresttheme <- theme_bw() +
  theme(
    legend.position = "none",
    axis.line = element_line(size = 0.4, color = "grey50"),
    axis.ticks = element_line(size = 0.3, color = "grey50"),
    axis.text = element_text(size = 9.5, color = "black"),
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank(),
    panel.border = element_blank(),
    strip.text = element_text(size = 9.5, color = "black")
    )

#++++++++++++++++++++++++++++++++++++++++++++++#
# Setting plot theme for forest plots
#++++++++++++++++++++++++++++++++++++++++++++++#
stackedbartheme <- theme(
    strip.text = element_text(size = 13),
    axis.text.y = element_text(size = 13),
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank(),
    axis.ticks.x = element_blank(),
    legend.text = element_text(size = 11),
    legend.background = element_rect("grey95")
  )

#++++++++++++++++++++++++++++++++++++++++++++++#
# Setting plot theme for regression scatterplot
#++++++++++++++++++++++++++++++++++++++++++++++#
regressiontheme <-   theme_bw() +
    theme(
      legend.position = "none",
      axis.ticks = element_line(size = 0.3, color = "grey50"),
      axis.text = element_text(size = 9.5, color = "black"),
      axis.text.y = element_blank(),
      panel.grid.major.y = element_blank(),
      panel.grid.minor = element_blank(),
      panel.border = element_rect(colour = "grey50", size = 0.7),
      strip.text = element_text(size = 9.5, color = "black")
      )

#++++++++++++++++++++++++++++++++++++++++++++++#
# Setting plot theme for orchard plot
#++++++++++++++++++++++++++++++++++++++++++++++#
orchardtheme <-  theme_bw() +
  theme(
    panel.grid.major.y = element_blank(),
    axis.text.y = element_text(size = 14, color = "black", face = "italic"),
    axis.text.x = element_text(size = 14, color = "grey20"),
    axis.title = element_text(size = 14, color = "black"),
    legend.position = c(0.15, 0.002),
    legend.justification = c(0.15, 0.002),
    legend.text = element_text(size = 14),
    legend.title = element_text(size = 14),
    legend.direction = "horizontal"
    )

#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++#
# Correcting estimate of binomial regression
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++#
c2 <- (16 * sqrt(3)/(15 * pi))^2

Hypotheses

tibble(
  Factors = c(
    "Genetic divergence",
    "Phenotypic divergence in focal trait",
    "Inviability of reciprocal hybrids (i.e. genetic incompatibility)",
    "Distribution overlap between parental species",
    "Crossing direction (parent-of-origin effect)",
    "Sex-determination system (male heterogamety vs. female heterogamety)",
    "Interaction between crossing direction and sex-determination system",
    "Trait type (sound trait vs. morphology)"
    ),
  'Hypothetical effect for novelty in phenotypic means' = c(
    "Increase. Positively correlate with the genetic divergence at phenotype-determining loci that increase phenotypic novelty through novel allelic interactions (i.e. over/underdominance, dominance, and epistasis) [@Lamkey1999; @Rieseberg1999]",
    "Increase. Indicates the genetic divergence at quantitative trait loci (QTL) that enhances phenotypic novelty [@Lamkey1999]",
    "Reduces trait size by outbreeding depression",
    "Increase. Reinforcement in range overlapping species pairs diverges QTL to develop behavioral isolation, and thus facilitate phenotypic novelty.",
    "Genomic imprinting increases novel phenotype resembles either mother or father (i.e. polar overdominance) [@Cockett1996].",
    "Increase in male heterogamety. In male heterogametic organisms, coadaptation between sex-chromosome and other chromosomes breaks down in F1 males [@Haldane1922; @Schilthuizen2011]. This genetic incompatibility reduce trait size in male heterogametic hybrids.",
    "Male heterogametic organisms tend to exhibit novel phenotype similar to father species, not mother species, due to effect of Y chromosome. Female heterogametic organisms do not show such trend.",
    "Increase in sound because of lower heritability compared to morphology [@Hoffmann2016]"
    ),
  `Hypothetical effect for phenotypic variability` = c(
    "Increase. Increase variation because of increased diversity in novel allelic interactions at phenotype determining loci [@Edmands1999]; increase variation by damaging developmental stability, through genetic incompatibility [@Lerner1954; @Alibert2003].",
    "Increase. Increases diversity in novel allelic interactions at QTL [@Edmands1999]",
    "Reduce. Lethal allelic interactions reduce genetic diversity of surviving hybrids",
    "Reduce. Reinforcement reduces intraspecific variation at QTL, and thus diminish genetic diversity of F1 hybrids at QTL",
    "Not specified",
    "Increase in male heterogamety. Reduced developmental stability increases phenotypic variability of heterogametic F1 males",
    "Not specified",
    "Not specified"
    )
  ) %>% 
  kable("html", caption = "Table S1. Hypotheses for phenotypic novelty and variability of F1 hybrids") %>% 
  kable_styling("striped", position = "left") %>% 
  scroll_box(width = "100%", height = "500px")

Table S1. Hypotheses for phenotypic novelty and variability of F1 hybrids

Factors	Hypothetical effect for novelty in phenotypic means	Hypothetical effect for phenotypic variability
Genetic divergence	Increase. Positively correlate with the genetic divergence at phenotype-determining loci that increase phenotypic novelty through novel allelic interactions (i.e. over/underdominance, dominance, and epistasis) (Lamkey and Edwards 1999; Rieseberg et al. 1999)	Increase. Increase variation because of increased diversity in novel allelic interactions at phenotype determining loci (Edmands 1999); increase variation by damaging developmental stability, through genetic incompatibility (Lerner 1954; Alibert and Auffray 2003).
Phenotypic divergence in focal trait	Increase. Indicates the genetic divergence at quantitative trait loci (QTL) that enhances phenotypic novelty (Lamkey and Edwards 1999)	Increase. Increases diversity in novel allelic interactions at QTL (Edmands 1999)
Inviability of reciprocal hybrids (i.e. genetic incompatibility)	Reduces trait size by outbreeding depression	Reduce. Lethal allelic interactions reduce genetic diversity of surviving hybrids
Distribution overlap between parental species	Increase. Reinforcement in range overlapping species pairs diverges QTL to develop behavioral isolation, and thus facilitate phenotypic novelty.	Reduce. Reinforcement reduces intraspecific variation at QTL, and thus diminish genetic diversity of F1 hybrids at QTL
Crossing direction (parent-of-origin effect)	Genomic imprinting increases novel phenotype resembles either mother or father (i.e. polar overdominance) (Cockett et al. 1996).	Not specified
Sex-determination system (male heterogamety vs. female heterogamety)	Increase in male heterogamety. In male heterogametic organisms, coadaptation between sex-chromosome and other chromosomes breaks down in F1 males (Haldane 1922; Schilthuizen et al. 2011). This genetic incompatibility reduce trait size in male heterogametic hybrids.	Increase in male heterogamety. Reduced developmental stability increases phenotypic variability of heterogametic F1 males
Interaction between crossing direction and sex-determination system	Male heterogametic organisms tend to exhibit novel phenotype similar to father species, not mother species, due to effect of Y chromosome. Female heterogametic organisms do not show such trend.	Not specified
Trait type (sound trait vs. morphology)	Increase in sound because of lower heritability compared to morphology (Hoffmann et al. 2016)	Not specified

Female preference data

tibble(
  Taxon = c(
    "Anura: tree frog (*Hyla*)",
    "Diptera: fruit fly (*Drosophila*)",
    "Orthoptera: bushcricket (*Ephippiger*)",
    "Orthoptera: grasshopper (*Chorthippus*)",
    "Orthoptera: grasshopper (*Chorthippus*)",
    "Orthoptera: grasshopper (*Chorthippus*)",
    "Orthoptera: Hawaiian cricket (*Laupala*)"
    ),
  `Parental species` = c(
    "*H. chrysoscelis* × *H. femoralis*",
    "*D. virilis* female × *D. montana* male",
    "*E. ephippiger* polysyllabic form 
    × *E. ephippiger* monosyllabic form",
    "*C. brunneus* × *C. jacobsi*",
    "*C. biguttulus* × *C. Brunneus*",
    "*C. parallelus* × *C. Montanus*",
    "*L. kohalensis* × *L. Paranigra*"
    ),
  `Reciprocal cross` = c("Viable", "Inviable", rep("Viable", 5)),
  Results = c(
    "**Dominance**. Both reciprocal hybrids preferred hybrids over one parental species but not over the other parental species",
    "**Maternal / paternal inheritance**. Resembled mother in their receptivity, but resembled father in their song requirement",
    "**Additive**. Intermediate mate preferences without a large difference between reciprocal crosses",
    "**Dominance and paternal inheritance**. Both reciprocal hybrids preferred one parental species over themselves. Preference function resembles that of the father",
    "**Additive and dominance**. Several components of preference showed dominance, but other components showed additive inheritance. No maternal/paternal inheritance.",
    "**Novel preference**. Both reciprocal hybrids did not discriminate between males of two parental species",
    "**Additive**. Intermediate preference function, which was similar to reciprocal hybrids, resulting in preference for hybrids"
    ),
  Reference = c(
    "@Doherty1984", "@Isoherranen1999", "@Ritchie2000", "@Bridle2006", 
    "@Gottsberger2019", "@Hochkirch2011", "@Shaw2000"
    )
  ) %>% 
  kable("html", caption = "Table S2. Previously described female mate preference of F1 hybrids") %>% 
  kable_styling("striped", position = "left") %>% 
  scroll_box(width = "100%", height = "500px")

Table S2. Previously described female mate preference of F1 hybrids

Taxon	Parental species	Reciprocal cross	Results	Reference
Anura: tree frog (Hyla)	H. chrysoscelis × H. femoralis	Viable	Dominance. Both reciprocal hybrids preferred hybrids over one parental species but not over the other parental species	Doherty and Gerhardt (1984)
Diptera: fruit fly (Drosophila)	D. virilis female × D. montana male	Inviable	Maternal / paternal inheritance. Resembled mother in their receptivity, but resembled father in their song requirement	Isoherranen et al. (1999)
Orthoptera: bushcricket (Ephippiger)	E. ephippiger polysyllabic form × E. ephippiger monosyllabic form	Viable	Additive. Intermediate mate preferences without a large difference between reciprocal crosses	Ritchie (2000)
Orthoptera: grasshopper (Chorthippus)	C. brunneus × C. jacobsi	Viable	Dominance and paternal inheritance. Both reciprocal hybrids preferred one parental species over themselves. Preference function resembles that of the father	Bridle et al. (2006)
Orthoptera: grasshopper (Chorthippus)	C. biguttulus × C. Brunneus	Viable	Additive and dominance. Several components of preference showed dominance, but other components showed additive inheritance. No maternal/paternal inheritance.	Gottsberger and Mayer (2019)
Orthoptera: grasshopper (Chorthippus)	C. parallelus × C. Montanus	Viable	Novel preference. Both reciprocal hybrids did not discriminate between males of two parental species	Hochkirch and Lemke (2011)
Orthoptera: Hawaiian cricket (Laupala)	L. kohalensis × L. Paranigra	Viable	Additive. Intermediate preference function, which was similar to reciprocal hybrids, resulting in preference for hybrids	Shaw (2000)

Only 3 out of 7 studies detected additive inheritance in female mate preference during hybridization, showing that non-additivity pervades female mate preference of F1 hybrids. Dominance and parent-of-origin effect (maternal / paternal inheritance) were detected in 3 and 2 studies, respectively. Novel weak preference also appeared in 1 study. Importantly, components of female preference often varied in inheritance mode, indicating that integration of mate preference easily breaks down in F1 hybrids. This summary is based on a non-exhaustive and non-systematic review.

Data description

List of primary studies included

read.xlsx(
  "../data/original.data.xlsx", sheet = "Primary.studies"
  # Same data is also saved as "../data/original.data.Primary.studies.txt"
  ) %>%
  arrange(First.author, Year) %>%
  kable("html") %>% kable_styling("striped", position = "left") %>% 
  scroll_box(width = "100%", height = "500px")

Study.name	First.author	Year	Title	Journal
Bentrey&Hoy_1972	Bentley, D. R.	1972	Genetic control of the neuronal network generating cricket (Teleogryllus gryllus) song patterns	Animal Behaviour
Blankers.et.al_2015	Blankers, T.	2015	Phenotypic variation and covariation indicate high evolvability of acoustic communication in crickets	Journal of Evolutionary Biology
Butlin&Hewitt_1988	Butlin, R. K.	1988	Genetics of behavioural and morphological differences between parapatric subspecies of Chorthippus parallelus (Orthoptera: Acrididae)	Biological Journal of the Linnean Society
Carson.et.al_1994	Carson, H. L.	1994	Change in male secondary sexual characters in artificial interspecific hybrid populations	Proceedings of the National Academy of Sciences of the United States of America
Coyne_1983	Coyne, J.A.	1983	Genetic basis of differences in genital morphology among three sibling species of Drosophila	Evolution
Coyne_1985	Coyne, J.A.	1985	Genetic studies of three sibling species of Drosophila with relationship to theories of speciation	Genetical Research
Coyne_1986	Coyne, J.A.	1986	Meiotic segregation and male recombination in interspecific hybrids of Drosophila	Genetics
Coyne.et.al_1991	Coyne, J.A.	1991	Genetics of morphological differences and hybrid sterility between Drosophila sechellia and its relatives	Genetical Research
Crapon&Fritzsch_1984	Crapon de caprona, MD.	1984	Interspecific fertile hybrids of haplochromine cichlidae (Teleostei) and their possible importance for speciation	Netherlands Journal of Zoology
Deregnaucourt_2010	Deregnaucourt, S.	2010	Interspecific hybridization as a tool to understand vocal divergence: The example of crowing in quail (Genus Coturnix)	PLoS ONE
Doherty & Gerhardt 1984	Doherty, J. A.	1984	Acoustic communication in hybrid treefrogs: sound production by males and selective phonotaxis by females	Journal of Comparative Physiology A
Ewing_1969	Ewing, A.W.	1969	The genetic basis of sound production in Drosophila pseudoobscura and D. persimilis	Animal Behaviour
Gadenne et al_1997	Gadenne, C.	1997	Development and pheromone communication systems in hybrids of Agrotis ipsilon and Agrotis segetum (Lepidoptera: Noctuidae)	Journal of Chemical Ecology
Gottsberger&Mayer_2007	Gottsberger, B.	2007	Behavioral sterility of hybrid males in acoustically communicating grasshoppers (Acrididae, Gomphocerinae)	Journal of Comparative Physiology A: Neuroethology, Sensory, Neural, and Behavioral Physiology
Magalhaes&Seehausen_2010	Magalhaes, I. S.	2010	Genetics of male nuptial colour divergence between sympatric sister species of a Lake Victoria cichlid fish	Journal of Evolutionary Biology
Monti et al_2001	Monti, L	2001	Elliptic fourier analysis of the form of genitalia in two Spodoptera species and their hybrids (Lepidoptera: Noctuidae)	Biological Journal of the Linnean Society
Musolf et al_2015	Musolf, K	2015	Ultrasonic vocalizations of male mice differ among species and females show assortative preferences for male calls	PLoS ONE
Paallysaho et al_2003	Paallysaho, S.	2003	Role of X chromosomal song genes in the evolution of species-specific courtship songs in Drosophila virilis group species	Behavior Genetics
Sasabe_et.al_2007	Sasabe, M.	2007	The genetic basis of interspecific differences in genital morphology of closely related carabid beetles	Heredity
Shaw_1996	Shaw, K. L.	1996	Polygenic inheritance of a behavioral phenotype: Interspecific genetics of song in the Hawaiian cricket genus Laupala	Evolution
Shaw_2000	Shaw, K. L.	2000	Interspecific genetics of mate recognition: Inheritance of female acoustic preference in Hawaiian crickets	Evolution
Suvanto et al_1994	Suvanto, L.	1994	Secondary courtship songs and inhibitory songs of Drosophila virilis-group males	Behavior Genetics
Tomaru&Oguma_1994	Tomaru, M.	1994	Differences in courtship song in the species of the Drosophila auraria complex	Animal Behaviour
van der Sluijs et al_2008	Van Der Sluijs, I.	2008	Female mating preference functions predict sexual selection against hybrids between sibling species of cichlid fish	Philosophical Transactions of the Royal Society B: Biological Sciences
Wells_1994	Wells, M. M.	1994	Behavioral responses of hybrid lacewings (Neuroptera: Chrysopidae) to courtship songs	Journal of Insect Behavior

Study.name: Including author name(s) and published year

Table of the dataset

Data of phenotype used in this study.

## Phenotype data ##
pheno <- read.xlsx(
    "../data/original.data.xlsx", sheet = "Phenotype"
    # Same data is also saved as "../data/original.data.Phenotype.txt"
    ) %>%
  # Excluding data without any parent data
  drop_na(contains("SD.sp")) %>%
  # to numeric
  mutate_at(
    vars(contains("Mn") | contains("SD") | N.sp1:N.sp2), 
    as.numeric
    ) %>%
  # delete observation with negative mean trait value
  filter(Mn.sp1 > 0 & Mn.sp2 > 0 & homologous == "Yes") %>%   
  # calculate phenotypic difference between parental species
  mutate(
    Pheno.divergence = abs(log(Mn.sp1/Mn.sp2))
    ) %>%
  mutate_at(vars(contains("pheno.div")), scale) %>%
  # describe whether full cross or not
  mutate(
    Reciprocal = ifelse(
      is.na(Mn.hyb12 & Mn.hyb21), 
      "Inviable", "Viable"
      )
    )

# making a scrollable table
kable(pheno, "html") %>% kable_styling("striped", position = "left") %>% 
    scroll_box(width = "100%", height = "500px")

Study.ID	Study.name	Source	Cross.ID	Genus	sp1.name	sp2.name	species.pair	homologous	trait.type	trait	Mn.sp1	SD.sp1	SE.sp1	Mn.hyb12	SD.hyb12	SE.hyb12	Mn.hyb21	SD.hyb21	SE.hyb21	Mn.sp2	SD.sp2	SE.sp2	Parents.sp1F.sp1	Parents.sp1M.sp1	Parents.sp1F.hyb12	Parents.sp1M.hyb21	Parents.sp2F.hyb21	Parents.sp2M.hyb12	Parents.sp2F.sp2	Parents.sp2M.sp2	N.sp1	N.hyb12	N.hyb21	N.sp2	reps.sp1	reps.hyb12	reps.hyb21	reps.sp2	Pheno.divergence	Reciprocal
s001	Blankers.et.al_2015	table1	Gryllus_rubens_Gryllus_texensis	Gryllus	Gryllus_rubens	Gryllus_texensis	Gryllus_rubens_Gryllus_texensis	Yes	sound	Carrier frequency	4.7300000	0.2700000		4.8200000	0.2700000		5.0100000	0.1700000		5.1800000	0.2200000				12	12	12	12			73	22	28	44					-0.544841228	Viable
s001	Blankers.et.al_2015	table1	Gryllus_rubens_Gryllus_texensis	Gryllus	Gryllus_rubens	Gryllus_texensis	Gryllus_rubens_Gryllus_texensis	Yes	sound	Pulse rate	45.3400000	3.8600000		55.2700000	3.9600000		61.9600000	2.7900000		66.8800000	5.4000000				12	12	12	12			73	22	28	44					-0.086102242	Viable
s001	Blankers.et.al_2015	table1	Gryllus_rubens_Gryllus_texensis	Gryllus	Gryllus_rubens	Gryllus_texensis	Gryllus_rubens_Gryllus_texensis	Yes	sound	Pulse duty cycle	0.4300000	0.0600000		0.4600000	0.0800000		0.4800000	0.0800000		0.4400000	0.0800000				12	12	12	12			73	22	28	44					-0.649410595	Viable
s001	Blankers.et.al_2015	table1	Gryllus_rubens_Gryllus_texensis	Gryllus	Gryllus_rubens	Gryllus_texensis	Gryllus_rubens_Gryllus_texensis	Yes	sound	Trill rate	0.2800000	0.1500000		0.5900000	0.2500000		0.6800000	0.3500000		1.2800000	0.3900000				12	12	12	12			73	22	28	44					1.656119587	Viable
s001	Blankers.et.al_2015	table1	Gryllus_rubens_Gryllus_texensis	Gryllus	Gryllus_rubens	Gryllus_texensis	Gryllus_rubens_Gryllus_texensis	Yes	sound	Trill duty cycle	0.7800000	0.1000000		0.7100000	0.1000000		0.7300000	0.1400000		0.6200000	0.1100000				12	12	12	12			73	22	28	44					-0.331214279	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityA	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	sound	EI	14.9600000	3.0958682		12.5000000	1.8973666		11.6000000	2.3216374		9.1888889	1.5863616										10	7	12	9					0.065883205	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityB	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	sound	EI	14.9600000	3.0958682		12.7666667	1.7461068		11.7400000	2.9499831		10.7880000	2.4703554										10	3	10	25					-0.181236570	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityA	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	sound	SI	15.1375000	11.2491875		0.5000000			9.7000000	6.2282522		11.8750000	6.6447348										8	1	10	6					-0.310936151	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityB	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	sound	SI	15.1375000	11.2491875		10.4250000	10.2530483		6.9305556	5.6248059		11.4291667	11.0768873										8	3	9	24					-0.251995019	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityA	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	sound	ES	31.6666667	26.6666667		60.7142857	12.9362645		43.6666667	18.5711844		59.4444444	25.4344959										10	7	15	9					0.285206116	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityB	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	sound	ES	31.6666667	26.6666667		31.6666667	9.4280904		52.2727273	18.1363067		44.1666667	25.3174292										10	3	11	10					-0.172364334	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityA	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	sound	SH	4.1538462	12.6217151		2.3571429	2.8996833		9.2142857	5.3375833		5.9545455	4.6390367										26	14	14	11					-0.130138747	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityB	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	sound	SH	4.1538462	12.6217151		4.7857143	5.0345743		7.9166667	5.5295921		7.3666667	5.0793700										26	7	12	30					0.197646553	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityA	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	sound	EH	63.0769231	66.4928701		108.5714286	102.7380263		194.2857143	83.6416067		200.0000000	120.3328717										26	14	14	10					1.092591646	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityB	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	sound	EH	63.0769231	66.4928701		77.1428571	73.6234213		223.0769231	116.8420772		181.3333333	123.2810700										26	7	13	30					0.941675478	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityA	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	sound	COH	4.9800000	5.9337678		7.8571429	6.9755404		6.4285714	5.0350810		2.5000000	3.9080337										25	14	14	11					0.376638926	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityB	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	sound	COH	4.9800000	5.9337678		3.5000000	4.8989795		10.1923077	8.6057639		3.7666667	4.6110977										25	8	13	30					-0.254716809	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityA	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	sound	COD	60.4615385	13.8153504		51.6000000	7.2000000		58.2857143	17.9181814		44.4000000	11.7575508										13	10	14	5					-0.209234963	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityB	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	sound	COD	60.4615385	13.8153504		66.0000000	9.7979590		55.0909091	11.9503103		43.5000000	14.6201915										13	3	11	16					-0.177692520	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityA	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	sound	EL	3.0400000	0.4983974		3.1857143	0.4517540		2.8076923	0.4269058		2.2250000	0.2633913										10	7	13	8					-0.204101811	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityB	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	sound	EL	3.0400000	0.4983974		2.6333333	0.1885618		2.9181818	0.3242283		2.1416667	0.5626697										10	3	11	24					-0.145305786	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityA	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	sound	NS	13.9000000	2.6153394		15.6666667	2.0344259		13.8846154	1.6426274		12.0000000	1.5000000										10	6	13	10					-0.458428548	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityB	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	sound	NS	13.9000000	2.6153394		12.1666667	0.4714045		13.6818182	1.1922615		12.7000000	2.4819347										10	3	11	25					-0.545754618	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityA	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	sound	SL	0.2270000	0.0124900		0.2121429	0.0174964		0.2065385	0.0274131		0.1987500	0.0264280										10	7	13	8					-0.480115420	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityB	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	sound	SL	0.2270000	0.0124900		0.2183333	0.0205480		0.2168182	0.0224897		0.1750000	0.0284376										10	3	11	23					-0.284098069	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityA	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	sound	COR	3.5000000	3.2730000		3.3181818	3.1298943		3.2500000	3.0741110		2.5000000	2.4074367										14	11	12	4					-0.166562938	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityB	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	sound	COR	3.5000000	3.2730000		3.0000000	2.8262465		2.5000000	2.3614732		1.6333333	1.4974284										14	2	11	15					0.489079925	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityA	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	morphology	RL	4.0142857	0.2799417		3.7545455	0.1724879		3.7428571	0.1545236		3.5571429	0.3499271										21	11	14	7					-0.498599402	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityB	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	morphology	RL	4.0142857	0.2799417		3.9111111	0.1940472		3.7000000	0.2449490		3.4481481	0.2283116										21	18	16	27					-0.450665719	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityA	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	morphology	NP	146.4285714	14.2380156		133.3333333	10.6718737		119.2857143	9.0350790		112.1428571	10.3015751										21	12	14	27					-0.273932094	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityB	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	morphology	NP	146.4285714	14.2380156		126.5000000	9.6306801		106.2500000	17.9843682		94.2592593	11.1971974										21	20	16	7					-0.006349655	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityA	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	morphology	PD	36.5294118	3.1081988		34.3333333	2.1081851		31.3636364	2.2268089		30.2000000	3.9191836										17	9	11	5					-0.391744730	Viable
s002	Butlin&Hewitt_1988	Fig1	parallelus_localityB	Chorthippus	Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	Yes	morphology	PD	36.5294118	3.1081988		32.5294118	2.1176471		29.3333333	3.5433819		27.2222222	1.8724777										17	17	12	18					-0.231852056	Viable
s003	Carson.et.al_1994	table4	Drosophila_heteroneura_Drosophila_silvestris	Drosophila	Drosophila_heteroneura	Drosophila_silvestris	Drosophila_heteroneura_Drosophila_silvestris	Yes	morphology	Number of tibial cilia on male foreleg	37.2500000	1.6500000		45.1500000	2.7600000		59.5700000	3.6700000		72.8500000	6.3300000		35	35	35	35	35	35	35	35	20	20	20	20					0.348315583	Viable
s004	Coyne_1983	table3	BGxMutant	Drosophila	Drosophila_mauritiana	Drosophila_simulans	Drosophila_mauritiana_Drosophila_simulans	Yes	morphology	GA	1442.0000000	202.6573463	37.000				5141.0000000	506.6665570	91.000	12166.0000000	735.0000000	147.0000	3	3	20	20	20	20	3	3	30	0	31	25					2.599978426	Inviable
s004	Coyne_1983	table3	BGxMutant	Drosophila	Drosophila_mauritiana	Drosophila_simulans	Drosophila_mauritiana_Drosophila_simulans	Yes	morphology	WL	462.0000000	10.9544512	2.000				487.0000000	11.1355287	2.000	477.0000000	10.0000000	2.0000	3	3	20	20	20	20	3	3	30	0	31	25					-0.635606580	Inviable
s004	Coyne_1983	table3	BGxMutant	Drosophila	Drosophila_mauritiana	Drosophila_simulans	Drosophila_mauritiana_Drosophila_simulans	Yes	morphology	GA/WL	3.1200000	0.4381780	0.080				10.5800000	2.0600728	0.370	25.4900000	1.5000000	0.3000	3	3	20	20	20	20	3	3	30	0	31	25					2.550441963	Inviable
s004	Coyne_1983	table3	BGxSPD(wildtype)	Drosophila	Drosophila_mauritiana	Drosophila_simulans	Drosophila_mauritiana_Drosophila_simulans	Yes	morphology	GA	1442.0000000	202.6573463	37.000	5139.0000000	471.0413995	86.000	5400.0000000	651.7898434	119.000	12050.0000000	1007.8095058	184.0000	3	3	20	20	20	20	3	3	30	30	30	30					2.585221856	Viable
s004	Coyne_1983	table3	BGxSPD(wildtype)	Drosophila	Drosophila_mauritiana	Drosophila_simulans	Drosophila_mauritiana_Drosophila_simulans	Yes	morphology	WL	462.0000000	10.9544512	2.000	497.0000000	10.9544512	2.000	487.0000000	10.9544512	2.000	1263.0000000	27.3861279	5.0000	3	3	20	20	20	20	3	3	30	30	30	30					0.864197270	Viable
s004	Coyne_1983	table3	BGxSPD(wildtype)	Drosophila	Drosophila_mauritiana	Drosophila_simulans	Drosophila_mauritiana_Drosophila_simulans	Yes	morphology	GA/WL	3.1200000	0.4381780	0.080	10.3500000	0.9859006	0.180	11.0900000	1.2597619	0.230	9.5380000	0.7558571	0.1380	3	3	20	20	20	20	3	3	30	30	30	30					1.036354019	Viable
s005	Coyne_1985	table1&2	BGxOxnard	Drosophila	Drosophila_mauritiana	Drosophila_simulans	Drosophila_mauritiana_Drosophila_simulans	Yes	morphology	Sex comb tooth number	14.3100000	1.1313708	0.080	12.3300000	1.0148892	0.100	12.1000000	0.9899495	0.070	9.8600000	0.8485281	0.0600									200	103	200	200					-0.111112971	Viable
s006	Coyne_1986	table1	FC	Drosophila	Drosophila_sechellia	Drosophila_simulans	Drosophila_sechellia_Drosophila_simulans	Yes	morphology	6T	176.0000000	44.5477272	10.500				142.0000000	36.9109740	8.700	15.7000000	2.6153394	0.6000					8	8			18		18	19					3.037736912	Inviable
s006	Coyne_1986	table1	FC	Drosophila	Drosophila_sechellia	Drosophila_simulans	Drosophila_sechellia_Drosophila_simulans	Yes	morphology	7T	49.3000000	12.3036580	2.900				393.9000000	97.5807358	23.000	335.2000000	61.0245852	14.0000					8	8			18		18	19					2.267571704	Inviable
s006	Coyne_1986	table1	FC	Drosophila	Drosophila_sechellia	Drosophila_simulans	Drosophila_sechellia_Drosophila_simulans	Yes	morphology	6Tratio	0.7790000	0.0169706	0.004				0.2640000	0.0169706	0.004	0.0490000	0.0305123	0.0070					8	8			18		18	19					3.575856725	Inviable
s006	Coyne_1986	table1	f^2	Drosophila	Drosophila_sechellia	Drosophila_simulans	Drosophila_sechellia_Drosophila_simulans	Yes	morphology	6T	176.0000000	44.5477272	10.500				142.7000000	11.1323852	2.700	17.9000000	6.1846584	1.5000					8	8			18		17	17					2.835746058	Inviable
s006	Coyne_1986	table1	f^2	Drosophila	Drosophila_sechellia	Drosophila_simulans	Drosophila_sechellia_Drosophila_simulans	Yes	morphology	7T	49.3000000	12.3036580	2.900				459.0000000	53.6003731	13.000	424.2000000	78.3390069	19.0000					8	8			18		17	17					2.630270622	Inviable
s006	Coyne_1986	table1	f^2	Drosophila	Drosophila_sechellia	Drosophila_simulans	Drosophila_sechellia_Drosophila_simulans	Yes	morphology	6Tratio	0.7790000	0.0169706	0.004				0.2380000	0.0164924	0.004	0.0400000	0.0123693	0.0030					8	8			18		17	17					3.888440179	Inviable
s006	Coyne_1986	table1	RMdet	Drosophila	Drosophila_sechellia	Drosophila_simulans	Drosophila_sechellia_Drosophila_simulans	Yes	morphology	6T	176.0000000	44.5477272	10.500				142.8000000	35.0000000	7.000	26.9000000	10.0697567	2.6000					8	8			18		25	15					2.208355177	Inviable
s006	Coyne_1986	table1	RMdet	Drosophila	Drosophila_sechellia	Drosophila_simulans	Drosophila_sechellia_Drosophila_simulans	Yes	morphology	7T	49.3000000	12.3036580	2.900				385.2000000	67.5000000	13.500	541.9000000	199.0713440	51.4000					8	8			18		25	15					3.007446168	Inviable
s006	Coyne_1986	table1	RMdet	Drosophila	Drosophila_sechellia	Drosophila_simulans	Drosophila_sechellia_Drosophila_simulans	Yes	morphology	6Tratio	0.7790000	0.0169706	0.004				0.2690000	0.0350000	0.007	0.0480000	0.0034857	0.0009					8	8			18		25	15					3.607615970	Inviable
s006	Coyne_1986	table1	RM	Drosophila	Drosophila_sechellia	Drosophila_simulans	Drosophila_sechellia_Drosophila_simulans	Yes	morphology	6T	176.0000000	44.5477272	10.500				184.2000000	50.0000000	10.000	25.0000000	5.4221767	1.4000					8	8			18		25	15					2.321180579	Inviable
s006	Coyne_1986	table1	RM	Drosophila	Drosophila_sechellia	Drosophila_simulans	Drosophila_sechellia_Drosophila_simulans	Yes	morphology	7T	49.3000000	12.3036580	2.900				381.7000000	73.0000000	14.600	612.6000000	115.4149037	29.8000					8	8			18		25	15					3.196330430	Inviable
s006	Coyne_1986	table1	RM	Drosophila	Drosophila_sechellia	Drosophila_simulans	Drosophila_sechellia_Drosophila_simulans	Yes	morphology	6Tratio	0.7790000	0.0169706	0.004				0.3220000	0.0400000	0.008	0.0390000	0.0023238	0.0006					8	8			18		25	15					3.927436409	Inviable
s008	Coyne.et.al_1991	table4	pn_j_irX85	Drosophila	Drosophila_mauritiana	Drosophila_sechellia	Drosophila_mauritiana_Drosophila_sechellia	Yes	morphology	Genital area	1.7460000	0.2100000	0.035	3.3230000	0.4501244	0.074	3.1040000	0.3710485	0.061	5.3110000	0.5231176	0.0860									36	37	37	37					1.028655549	Viable
s008	Coyne.et.al_1991	table4	pn_j_irX85	Drosophila	Drosophila_mauritiana	Drosophila_sechellia	Drosophila_mauritiana_Drosophila_sechellia	Yes	morphology	Tibia length	0.4410000	0.0120000	0.002	0.4740000	0.0121655	0.002	0.4870000	0.0182483	0.003	0.4690000	0.0182483	0.0030									36	37	37	37					-0.590004930	Viable
s009	Crapon&Fritzsch_1984	table4	Haplochromis_burtoni_Haplochromis_nubilus	Haplochromis	Haplochromis_burtoni	Haplochromis_nubilus	Haplochromis_burtoni_Haplochromis_nubilus	Yes	morphology	egg dummy size	8.9000000	0.7000000		10.0000000	1.6000000					8.3000000	0.8000000										48	34		58					-0.577316397	Inviable
s009	Crapon&Fritzsch_1984	table4	Haplochromis_burtoni_Haplochromis_nubilus	Haplochromis	Haplochromis_burtoni	Haplochromis_nubilus	Haplochromis_burtoni_Haplochromis_nubilus	Yes	morphology	egg dummy number	6.7000000	1.8000000		4.3000000	1.0000000					2.5000000	0.6000000										48	34		58					0.833602237	Inviable
s009	Crapon&Fritzsch_1984	table4	Haplochromis_burtoni_Haplochromis_nubilus	Haplochromis	Haplochromis_burtoni	Haplochromis_nubilus	Haplochromis_burtoni_Haplochromis_nubilus	Yes	morphology	egg dummy position	1.4000000	0.6000000		3.9000000	1.2000000					5.4000000	0.6000000										48	34		58					1.394429398	Inviable
s010	Deregnaucourt_2010	table2	Coturnix_coturnix_coturnix_Coturnix_japonica	Coturnix	Coturnix_coturnix_coturnix	Coturnix_japonica	Coturnix_coturnix_coturnix_Coturnix_japonica	Yes	sound	S2	172.0000000	86.6833317	17.000	168.0000000	59.2368129	11.000	182.0000000	54.7448628	9.000	59.0000000	79.3725393	15.0000			8	8	15	15			26	29	37	28					0.963200823	Viable
s11	Doherty & Gerhardt 1984	fig4	Hyla_chrysoscelis_Hyla_femoralis	Hyla	Hyla_chrysoscelis	Hyla_femoralis	Hyla_chrysoscelis_Hyla_femoralis	Yes	sound	pulse/call	29.3023256	3.2558140		15.3488372	4.1860465		20.0000000	9.3023256		4.2420000	0.6225769										5	5	4	5					2.291952220	Viable
s11	Doherty & Gerhardt 1984	fig4	Hyla_chrysoscelis_Hyla_femoralis	Hyla	Hyla_chrysoscelis	Hyla_femoralis	Hyla_chrysoscelis_Hyla_femoralis	Yes	sound	pulse duration	15.3191489	1.2765957		17.0212766	8.5106383		17.8723404	7.2340426		48.5106383	7.2340426										6	6	5	5					1.090615675	Viable
s11	Doherty & Gerhardt 1984	fig4	Hyla_chrysoscelis_Hyla_femoralis	Hyla	Hyla_chrysoscelis	Hyla_femoralis	Hyla_chrysoscelis_Hyla_femoralis	Yes	sound	pulse rise	7.6068376	1.1965812		2.3931624	1.8803419		1.9658120	1.1965812		1.3675214	0.4273504										6	6	5	5					1.958354051	Viable
s11	Doherty & Gerhardt 1984	fig4	Hyla_chrysoscelis_Hyla_femoralis	Hyla	Hyla_chrysoscelis	Hyla_femoralis	Hyla_chrysoscelis_Hyla_femoralis	Yes	sound	pulse decay	7.7142857	2.0571429		10.2857143	4.8857143		8.4000000	3.2571429		4.8000000	1.7142857										6	5	5	5					0.045972199	Viable
s11	Doherty & Gerhardt 1984	fig4	Hyla_chrysoscelis_Hyla_femoralis	Hyla	Hyla_chrysoscelis	Hyla_femoralis	Hyla_chrysoscelis_Hyla_femoralis	Yes	sound	pulse period	26.6304348	5.9782609		78.2608696	23.9130435		80.4347826	15.7608696		157.6086957	60.3260870										5	5	5	5					2.053870707	Viable
s12	Ewing_1969	table2	Drosophila_persimilis_Drosophila_pseudoobscura	Drosophila	Drosophila_persimilis	Drosophila_pseudoobscura	Drosophila_persimilis_Drosophila_pseudoobscura	Yes	sound	Mean HRR song pulse intervals in msec	66.8000000	10.9000000		48.7000000	7.9000000		56.8000000	7.3000000		42.6000000	4.8000000				25	25	25	25			4	4	4	4	386	273	268	281	0.008067489	Viable
s13	van der Sluijs et al_2008	table1	Pundamilia_nyererei_Pundamilia_pundamilia	Pundamilia	Pundamilia_nyererei	Pundamilia_pundamilia	Pundamilia_nyererei_Pundamilia_pundamilia	Yes	morphology	colour score	3.8900000	0.2121320	0.050	2.2100000	0.8668333	0.170	2.2500000	0.8049845	0.180	0.2100000	0.3487119	0.0800									18	26	20	19					3.811311702	Viable
s13	van der Sluijs et al_2008	table1	Pundamilia_nyererei_Pundamilia_pundamilia	Pundamilia	Pundamilia_nyererei	Pundamilia_pundamilia	Pundamilia_nyererei_Pundamilia_pundamilia	Yes	morphology	n bars	6.3600000	0.4666905	0.110	5.3500000	0.8668333	0.170	5.9500000	0.4919350	0.110	5.1100000	0.6538348	0.1500									18	26	20	19					-0.347765189	Viable
s14	Gadenne et al_1997	table1	Agrotis_ipsilon_Agrotis_segetum	Agrotis	Agrotis_ipsilon	Agrotis_segetum	Agrotis_ipsilon_Agrotis_segetum	Yes	morphology	Harp_h1	328.2000000	29.9000000		440.5000000	35.3000000					611.5000000	79.6000000		>100	>100	4	4	0	0	>100	>100	30	24	0	25					0.273675987	Inviable
s14	Gadenne et al_1997	table1	Agrotis_ipsilon_Agrotis_segetum	Agrotis	Agrotis_ipsilon	Agrotis_segetum	Agrotis_ipsilon_Agrotis_segetum	Yes	morphology	Harp_h2	1029.7000000	41.5000000		861.4000000	40.4000000					750.3000000	44.8000000		>100	>100	4	4	0	0	>100	>100	30	24	0	25					-0.197249077	Inviable
s14	Gadenne et al_1997	table1	Agrotis_ipsilon_Agrotis_segetum	Agrotis	Agrotis_ipsilon	Agrotis_segetum	Agrotis_ipsilon_Agrotis_segetum	Yes	morphology	Harp_h1/h2	0.3200000	0.0300000		0.5100000	0.0300000					0.8000000	0.1000000		>100	>100	4	4	0	0	>100	>100	30	24	0	25					0.726513411	Inviable
s14	Gadenne et al_1997	table1	Agrotis_ipsilon_Agrotis_segetum	Agrotis	Agrotis_ipsilon	Agrotis_segetum	Agrotis_ipsilon_Agrotis_segetum	Yes	morphology	Valva_v1	608.0000000	44.5000000		672.7000000	70.0000000					732.2000000	73.9000000		>100	>100	4	4	0	0	>100	>100	30	24	0	25					-0.398517284	Inviable
s14	Gadenne et al_1997	table1	Agrotis_ipsilon_Agrotis_segetum	Agrotis	Agrotis_ipsilon	Agrotis_segetum	Agrotis_ipsilon_Agrotis_segetum	Yes	morphology	Valva_v2	891.1000000	69.6000000		817.7000000	74.0000000					818.0000000	84.8000000		>100	>100	4	4	0	0	>100	>100	30	24	0	25					-0.552982374	Inviable
s14	Gadenne et al_1997	table1	Agrotis_ipsilon_Agrotis_segetum	Agrotis	Agrotis_ipsilon	Agrotis_segetum	Agrotis_ipsilon_Agrotis_segetum	Yes	morphology	Valva_v1/v2	0.6800000	0.0400000		0.8200000	0.0800000					0.9000000	0.0700000		>100	>100	4	4	0	0	>100	>100	30	24	0	25					-0.253080256	Inviable
s14	Gadenne et al_1997	table1	Agrotis_ipsilon_Agrotis_segetum	Agrotis	Agrotis_ipsilon	Agrotis_segetum	Agrotis_ipsilon_Agrotis_segetum	Yes	morphology	Valva_v3	3696.4000000	134.5000000		3387.1000000	125.0000000					3314.2000000	143.4000000		>100	>100	4	4	0	0	>100	>100	30	24	0	25					-0.516710910	Inviable
s14	Gadenne et al_1997	table1	Agrotis_ipsilon_Agrotis_segetum	Agrotis	Agrotis_ipsilon	Agrotis_segetum	Agrotis_ipsilon_Agrotis_segetum	Yes	morphology	Clasper surface	269.6000000	12.4000000		317.4000000	17.5000000					415.9000000	31.4000000		>100	>100	4	4	0	0	>100	>100	30	24	0	25					-0.017105678	Inviable
s14	Gadenne et al_1997	table1	Agrotis_ipsilon_Agrotis_segetum	Agrotis	Agrotis_ipsilon	Agrotis_segetum	Agrotis_ipsilon_Agrotis_segetum	Yes	morphology	Juxta_j1	437.0000000	33.8000000		329.8000000	20.5000000					281.1000000	25.2000000		>100	>100	4	4	0	0	>100	>100	30	24	0	25					-0.005219039	Inviable
s14	Gadenne et al_1997	table1	Agrotis_ipsilon_Agrotis_segetum	Agrotis	Agrotis_ipsilon	Agrotis_segetum	Agrotis_ipsilon_Agrotis_segetum	Yes	morphology	Juxta_j2	164.0000000	24.0000000		194.7000000	23.8000000					234.6000000	21.2000000		>100	>100	4	4	0	0	>100	>100	30	24	0	25					-0.133380509	Inviable
s14	Gadenne et al_1997	table1	Agrotis_ipsilon_Agrotis_segetum	Agrotis	Agrotis_ipsilon	Agrotis_segetum	Agrotis_ipsilon_Agrotis_segetum	Yes	morphology	Juxta_j1/j2	0.3800000	0.0700000		0.5900000	0.0900000					0.8400000	0.1100000		>100	>100	4	4	0	0	>100	>100	30	24	0	25					0.536967787	Inviable
s14	Gadenne et al_1997	table1	Agrotis_ipsilon_Agrotis_segetum	Agrotis	Agrotis_ipsilon	Agrotis_segetum	Agrotis_ipsilon_Agrotis_segetum	Yes	morphology	Vesica teeth	17.0000000	3.2000000		27.6000000	5.8000000					40.5000000	5.1000000		>100	>100	4	4	0	0	>100	>100	30	24	0	25					0.652269216	Inviable
s15	Gottsberger&Mayer_2007	table1	Chorthippus_biguttulus_Chorthippus_brunneus	Chorthippus	Chorthippus_biguttulus	Chorthippus_brunneus	Chorthippus_biguttulus_Chorthippus_brunneus	Yes	sound	Song duration	11.8400000	3.7000000		8.3400000	3.4500000		12.5400000	3.3700000		12.2600000	4.8200000				6	6	3	3			35	19	18	22					-0.631129479	Viable
s15	Gottsberger&Mayer_2007	table1	Chorthippus_biguttulus_Chorthippus_brunneus	Chorthippus	Chorthippus_biguttulus	Chorthippus_brunneus	Chorthippus_biguttulus_Chorthippus_brunneus	Yes	sound	Phrase duration	2.0700000	0.3200000		0.4500000	0.0600000		0.5600000	0.1500000		0.1800000	0.0400000				6	6	3	3			35	19	18	22					3.077050352	Viable
s15	Gottsberger&Mayer_2007	table1	Chorthippus_biguttulus_Chorthippus_brunneus	Chorthippus	Chorthippus_biguttulus	Chorthippus_brunneus	Chorthippus_biguttulus_Chorthippus_brunneus	Yes	sound	Phrase pause	2.0600000	0.5700000		1.7400000	0.4500000		1.9400000	0.3900000		1.6600000	0.5400000				6	6	3	3			35	19	18	22					-0.352294492	Viable
s15	Gottsberger&Mayer_2007	table1	Chorthippus_biguttulus_Chorthippus_brunneus	Chorthippus	Chorthippus_biguttulus	Chorthippus_brunneus	Chorthippus_biguttulus_Chorthippus_brunneus	Yes	sound	Number of phrases	3.2000000	0.9000000		4.7000000	1.7000000		5.9000000	1.6000000		7.8000000	2.4000000				6	6	3	3			35	19	18	22					0.687517181	Viable
s15	Gottsberger&Mayer_2007	table1	Chorthippus_biguttulus_Chorthippus_brunneus	Chorthippus	Chorthippus_biguttulus	Chorthippus_brunneus	Chorthippus_biguttulus_Chorthippus_brunneus	Yes	sound	Syllables per phrase	32.5000000	6.9000000		3.4000000	1.8000000		2.0000000	0.7000000		5.3000000	1.0000000				6	6	3	3			35	19	18	22					2.108508114	Viable
s15	Gottsberger&Mayer_2007	table1	Chorthippus_biguttulus_Chorthippus_brunneus	Chorthippus	Chorthippus_biguttulus	Chorthippus_brunneus	Chorthippus_biguttulus_Chorthippus_brunneus	Yes	sound	Pulses per syllable	6.2000000	0.5000000		20.2000000	16.9000000		32.0000000	10.5000000		3.1000000	0.1000000				6	6	3	3			35	19	18	22					0.382812357	Viable
s15	Gottsberger&Mayer_2007	table1	Chorthippus_biguttulus_Chorthippus_brunneus	Chorthippus	Chorthippus_biguttulus	Chorthippus_brunneus	Chorthippus_biguttulus_Chorthippus_brunneus	Yes	sound	Syllable period	60.1000000	7.0000000		193.8000000	156.7000000		257.7000000	106.8000000		29.6000000	4.5000000				6	6	3	3			35	19	18	22					0.406052394	Viable
s15	Gottsberger&Mayer_2007	table1	Chorthippus_biguttulus_Chorthippus_brunneus	Chorthippus	Chorthippus_biguttulus	Chorthippus_brunneus	Chorthippus_biguttulus_Chorthippus_brunneus	Yes	sound	Pulse period	8.6000000	1.2000000		7.9000000	0.4000000		8.0000000	0.5000000		8.8000000	1.4000000				6	6	3	3			35	19	18	22					-0.649410595	Viable
s15	Gottsberger&Mayer_2007	table1	Chorthippus_biguttulus_Chorthippus_brunneus	Chorthippus	Chorthippus_biguttulus	Chorthippus_brunneus	Chorthippus_biguttulus_Chorthippus_brunneus	Yes	sound	Phase-shift in	85.9000000	15.2000000		159.6000000	28.0000000		165.0000000	24.4000000		131.9000000	15.6000000				6	6	3	3			35	19	18	22					-0.024260592	Viable
s16	Bentrey&Hoy_1972	table1	Teleogryllus_commodus_Teleogryllus_oceanicus	Teleogryllus	Teleogryllus_commodus	Teleogryllus_oceanicus	Teleogryllus_commodus_Teleogryllus_oceanicus	Yes	sound	Intra-chirp interval	52.1000000	6.8000000		60.4000000	6.1000000		57.5000000	6.5000000		66.8000000	6.7000000		12	12	12	12	12	12	12	12	5	10	10	5	489	807	712	290	-0.302005098	Viable
s16	Bentrey&Hoy_1972	table1	Teleogryllus_commodus_Teleogryllus_oceanicus	Teleogryllus	Teleogryllus_commodus	Teleogryllus_oceanicus	Teleogryllus_commodus_Teleogryllus_oceanicus	Yes	sound	Intra-trill interval	31.7000000	3.5000000		36.7000000	5.0000000		33.0000000	3.8000000		41.0000000	1.8000000		12	12	12	12	12	12	12	12	5	10	10	5	1840	2450	2833	740	-0.288578184	Viable
s16	Bentrey&Hoy_1972	table1	Teleogryllus_commodus_Teleogryllus_oceanicus	Teleogryllus	Teleogryllus_commodus	Teleogryllus_oceanicus	Teleogryllus_commodus_Teleogryllus_oceanicus	Yes	sound	Inter-trill interval	160.9000000	50.6000000		154.0000000	38.8000000		136.9000000	23.6000000		122.8000000	14.1000000		12	12	12	12	12	12	12	12	5	10	10	5	119	693	857	813	-0.268599892	Viable
s16	Bentrey&Hoy_1972	table1	Teleogryllus_commodus_Teleogryllus_oceanicus	Teleogryllus	Teleogryllus_commodus	Teleogryllus_oceanicus	Teleogryllus_commodus_Teleogryllus_oceanicus	Yes	sound	No. of A-pulses per chirp	5.9000000	0.9000000		4.9000000	0.7000000		4.2000000	0.6000000		4.8000000	0.9000000		12	12	12	12	12	12	12	12	5	10	10	5	100	100	100	100	-0.367007059	Viable
s16	Bentrey&Hoy_1972	table1	Teleogryllus_commodus_Teleogryllus_oceanicus	Teleogryllus	Teleogryllus_commodus	Teleogryllus_oceanicus	Teleogryllus_commodus_Teleogryllus_oceanicus	Yes	sound	No. of B-pulses per trill	10.7000000	5.3000000		4.9000000	2.0000000		4.5000000	1.5000000		2.0000000	0.1000000		12	12	12	12	12	12	12	12	5	10	10	5	147	200	194	250	1.898358907	Viable
s16	Bentrey&Hoy_1972	table1	Teleogryllus_commodus_Teleogryllus_oceanicus	Teleogryllus	Teleogryllus_commodus	Teleogryllus_oceanicus	Teleogryllus_commodus_Teleogryllus_oceanicus	Yes	sound	No. of B-pulses per phrase	21.5000000	10.4000000		19.4000000	4.7000000		18.4000000	4.9000000		20.9000000	3.0000000		12	12	12	12	12	12	12	12	5	10	10	5	34	37	28	38	-0.641225209	Viable
s16	Bentrey&Hoy_1972	table1	Teleogryllus_commodus_Teleogryllus_oceanicus	Teleogryllus	Teleogryllus_commodus	Teleogryllus_oceanicus	Teleogryllus_commodus_Teleogryllus_oceanicus	Yes	sound	No. of trills per phrase	2.3000000	1.2000000		4.8000000	1.2000000		4.2000000	1.0000000		9.4000000	2.3000000		12	12	12	12	12	12	12	12	5	10	10	5	100	100	100	100	1.483570683	Viable
s16	Bentrey&Hoy_1972	table1	Teleogryllus_commodus_Teleogryllus_oceanicus	Teleogryllus	Teleogryllus_commodus	Teleogryllus_oceanicus	Teleogryllus_commodus_Teleogryllus_oceanicus	Yes	sound	Phrase repetition rate	41.7000000	18.1000000		33.0000000	7.0000000		37.5000000	5.3000000		28.5000000	2.4000000		12	12	12	12	12	12	12	12	5	10	10	5	102	70	105	77	-0.098598877	Viable
s16	Bentrey&Hoy_1972	table2	Gryllus_armatus_Gryllus_rubens	Gryllus	Gryllus_armatus	Gryllus_rubens	Gryllus_armatus_Gryllus_rubens	Yes	sound	IPI	14.6000000	1.7000000		17.4000000	2.0000000					19.8000000	3.6000000		12	12	12	12	12	12	12	12	5	5		5	785	837		439	-0.215561704	Inviable
s16	Bentrey&Hoy_1972	table3	Gryllus_armatus_Gryllus_rubens	Gryllus	Gryllus_armatus	Gryllus_rubens	Gryllus_armatus_Gryllus_rubens	Yes	sound	Chirp length	3.0095000	0.4045488		3.0256500	1.1668687					120.8835000	98.8883015										4	6		5	1016	2275		1027	5.003474245	Inviable
s16	Bentrey&Hoy_1972	table3	Gryllus_campestris_Gryllus_rubens	Gryllus	Gryllus_campestris	Gryllus_rubens	Gryllus_campestris_Gryllus_rubens	Yes	sound	Chirp length	6.4650000	0.3482456		16.1350000	11.5580827					120.8835000	98.8883015										4	1		5	1007	206		1027	3.825739945	Inviable
s17	Magalhaes&Seehausen_2010	Fig2	Pundamilia_nyererei_Pundamilia_pundamilia	Pundamilia	Pundamilia_nyererei	Pundamilia_pundamilia	Pundamilia_nyererei_Pundamilia_pundamilia	Yes	morphology	% red cover of body	26.2272727	8.3349310		6.8750000	6.0194996		7.1000000	5.1224994		2.4500000	1.6575584				4	4	3	3			11	24	5	20					2.966712815	Viable
s18	Monti et al_2001	table1	Spodoptera_descoinsi_Spodoptera_latifascia	Spodoptera	Spodoptera_descoinsi	Spodoptera_latifascia	Spodoptera_descoinsi_Spodoptera_latifascia	Yes	morphology	clasper shape	977.9300000	56.8600000		1097.0000000	42.5200000		1080.8300000	66.6200000		1117.0000000	70.4700000		<15	<15	<15	<15	<15	<15	<15	<15	30	30	30	30					-0.480020782	Viable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_meanduration	0.0350000	0.0120277		0.0345000	0.0021213					0.0381667	0.0110529										4	2		6					-0.551411061	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_peakfrequstart	88243.2360000	13841.1853318		74950.0000000	636.3961031					72556.1870000	12617.0280666										4	2		6					-0.383334895	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_minfreqstart	86607.4352500	13818.4480691		73428.1250000	667.3320247					69262.5583333	11355.7727478										4	2		6					-0.340599520	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_maxfrequstart	91795.0325000	12995.6534046		77565.6250000	48.6135912					78189.1510000	12300.4108541										4	2		6					-0.437719496	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_bandwstart	5143.7662500	2045.3369815		4068.7500000	662.9126074					8864.5688333	4581.0818004										4	2		6					0.153511885	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_entropystart	0.2057500	0.0209185		0.1930000	0.0325269					0.2135000	0.0154370										4	2		6					-0.627869204	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_peakfrequend	74352.4027500	5053.6317335		73462.5000000	18685.2966929					70818.2011667	7761.6449976										4	2		6					-0.609809630	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_minfreqend	72380.2272500	5077.6205582		71778.1250000	18353.8403892					68704.3900000	7683.0440288										4	2		6					-0.604541730	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_maxfrequend	77172.7707500	5578.8877045		76237.5000000	19498.4694912					73259.6543333	7707.3985467										4	2		6					-0.604670211	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_bandwend	4737.1212500	658.3624192		4400.0000000	1131.3708499					4521.8338333	641.0389018										4	2		6					-0.613179660	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_entropyend	0.2087500	0.0171925		0.2030000	0.0367696					0.2091667	0.0126399										4	2		6					-0.681749308	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_peakfrequcentre	85535.6927500	9125.8577495		62084.3750000	37454.5623160					61978.2051667	9275.5460027										4	2		6					-0.188621563	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_minfreqcenter	80507.1970000	8382.7388396		69421.8750000	21244.1393573					61213.2866667	9007.7450086										4	2		6					-0.262814215	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_maxfreqcenter	90638.4525000	8209.6444716		95231.2500000	2713.5222728					71257.7506667	7370.4746511										4	2		6					-0.314270540	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_bandwcentre	10071.6775000	4429.0518530		25728.1250000	23931.1451258					9988.7530000	6829.4344123										4	2		6					-0.672086435	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_entropycentre	0.2252500	0.0343256		0.1705000	0.0912168					0.2091667	0.0341433										4	2		6					-0.570718267	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_peakfrequmax	80739.4265000	5303.4742393		82650.0000000	3676.9552622					68118.2983333	9187.5342106										4	2		6					-0.423003987	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_minfreqmax	79202.7922500	5361.1342413		81371.8750000	2930.0737245					66305.2156667	9378.6307948										4	2		6					-0.411048644	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_maxfrequmax	84045.2057500	4837.5806504		85321.8750000	3707.8911838					72361.4995000	9750.2056443										4	2		6					-0.454272527	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_bandwmax	4802.3810000	1332.0316481		3918.7500000	733.6232855					6007.6146667	2379.1891824										4	2		6					-0.339929894	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_entropymax	0.1950000	0.0107393		0.1925000	0.0162635					0.2043333	0.0116390										4	2		6					-0.612808308	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_FAC2_1	1.9098486	1.8018551		0.4389270	1.0762664					0.4527336	1.2603555										4	2		6					1.532358568	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_FAC1_3	1.4977934	0.9941282		1.3129292	0.3134363					3.8618017	3.4780153										4	2		6					0.774030955	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_FAC1_4	0.4874387	1.1538739		-0.6730174	2.7297053					0.5559659	0.6476944										4	2		6					-0.482209844	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped Inverted_meanduration	0.0247500	0.0116154		0.0330000						0.0431429	0.0216597										4	1		7					0.171093472	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped Inverted_peakfrequstart	76228.5715000	13271.3152368		75540.0000000						54580.9524286	7151.3235607										4	1		7					-0.170291706	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped Inverted_minfreqstart	74332.1427500	12615.6156919		74460.0000000						53274.7618571	6927.0360415										4	1		7					-0.171786727	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped Inverted_maxfrequstart	78808.3332500	12749.5641588		78860.0000000						57440.0001429	7073.5971258										4	1		7					-0.197667775	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped Inverted_bandwstart	4411.9050000	626.2642551		4340.0000000						4110.0000000	600.3116768										4	1		7					-0.575640926	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped Inverted_entropystart	0.2115000	0.0157586		0.2210000						0.2022857	0.0233503										4	1		7					-0.616211094	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped Inverted_peakfrequend	75334.5240000	11042.2134461		79060.0000000						58608.0951429	6273.4698049										4	1		7					-0.298111985	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped Inverted_minfreqend	74025.0000000	11169.4150847		78160.0000000						57369.0475714	6615.9479668										4	1		7					-0.292209258	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped Inverted_maxfrequend	77971.4285000	11022.6693431		82940.0000000						61825.7142857	6094.1748961										4	1		7					-0.327442700	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped Inverted_bandwend	3878.5715000	836.3032833		4740.0000000						4394.2855714	1103.0087703										4	1		7					-0.492536481	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped Inverted_entropyend	0.1935000	0.0287112		0.2140000						0.2121429	0.0128508										4	1		7					-0.543143026	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped Inverted_peakfrequcentre	84771.4285000	11250.2360323		90260.0000000						65132.8571429	6639.2899236										4	1		7					-0.278914487	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped Inverted_minfreqcenter	82939.2857500	11362.3125349		89020.0000000						63461.4287143	7232.0898611										4	1		7					-0.272526799	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped Inverted_maxfreqcenter	87214.2857500	11278.1558979		92820.0000000						68880.9524286	4518.6205675										4	1		7					-0.321334970	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped Inverted_bandwcentre	4239.2857500	426.0486747		3800.0000000						5404.2857143	4186.7844922										4	1		7					-0.310847230	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped Inverted_entropycentre	0.1967500	0.0115289		0.1930000						0.1890000	0.0072801										4	1		7					-0.622922047	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped Inverted_peakfrequmax	81715.4760000	10451.3541605		85680.0000000						64371.4285714	7250.3760465										4	1		7					-0.317353264	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped Inverted_minfreqmax	80088.0952500	10219.5703381		84440.0000000						62977.1428571	7156.8075149										4	1		7					-0.314608750	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped Inverted_maxfrequmax	84541.6665000	10896.6653124		88540.0000000						66872.3810000	7126.6805948										4	1		7					-0.323691569	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped Inverted_bandwmax	4390.4762500	1075.8253068		4080.0000000						3884.2855714	142.6563258										4	1		7					-0.496139807	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped Inverted_entropymax	0.1997500	0.0209821		0.1990000						0.1897381	0.0127089										4	1		7					-0.605617070	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped Inverted_FAC1_2	0.0641031	0.7688575		0.8763980						0.3896661	1.0029075										4	1		7					2.095052447	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_meanduration	0.0260000	0.0116619		0.0376000	0.0112827					0.0354444	0.0097482										4	5		9					-0.207537553	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_peakfrequstart	80479.7617500	12733.6564666		79085.4762000	3447.3265538					65840.7107778	5732.7881166										4	5		9					-0.375584876	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_minfreqstart	79164.8807500	12512.0095023		77460.9522000	3600.6844370					63745.1970000	5888.2100068										4	5		9					-0.351138435	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_maxfrequstart	82837.5000000	12669.1982077		81530.7144000	3665.7522184					68163.0446667	5734.4353543										4	5		9					-0.384501627	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_bandwstart	3639.8810000	853.3854609		4006.1906000	651.1667981					4360.5252222	453.4732575										4	5		9					-0.406584124	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_entropystart	0.1835000	0.0233880		0.1972000	0.0226208					0.2200000	0.0225278										4	5		9					-0.405396032	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_peakfrequend	79257.1427500	12462.3047588		80213.0952000	4487.0077220					66126.1151111	6271.9991028										4	5		9					-0.405825913	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_minfreqend	77598.8095000	12226.8021838		78693.8096000	4816.5305116					64482.5417778	5952.0201006										4	5		9					-0.399628204	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_maxfrequend	81726.7857500	12753.0766460		83136.1904000	4656.4245579					69003.3801111	5730.3578037										4	5		9					-0.424166620	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_bandwend	4083.3335000	786.2241599		4392.8570000	725.5012887					4462.8988889	468.0169154										4	5		9					-0.547914084	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_entropyend	0.1977500	0.0196193		0.2048000	0.0207292					0.2275556	0.0441478										4	5		9					-0.468581058	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_peakfrequcentre	74933.3332500	10968.5408399		71673.8094000	3009.5076498					59479.1160000	5400.4655878										4	5		9					-0.329059247	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_minfreqcenter	72616.6667500	12624.6233365		70144.2856000	2999.7169555					57936.3224444	5682.7417545										4	5		9					-0.336951003	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_maxfreqcenter	80083.9285000	8479.2792799		74386.4286000	3202.4808429					62278.1645556	5499.6658025										4	5		9					-0.297497989	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_bandwcentre	7431.5477500	6279.7280238		4214.0476000	363.2867758					4307.3395556	818.6217348										4	5		9					0.155263093	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_entropycentre	0.1990000	0.0053541		0.1952000	0.0056303					0.2087778	0.0237106										4	5		9					-0.610940719	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_peakfrequmax	79097.6192500	11822.7651521		76354.7620000	5142.8431679					63787.8418889	5091.3037702										4	5		9					-0.353477742	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_minfreqmax	77244.0475000	11659.2282489		74737.8572000	5116.6142024					62107.5704444	4788.4915589										4	5		9					-0.348884971	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_maxfrequmax	81376.1905000	11690.8610379		78997.6190000	5192.4923151					66376.4416667	5054.8521007										4	5		9					-0.371005446	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_bandwmax	4088.6907500	268.9063550		4235.0000000	620.6398924					4223.3141111	397.7357504										4	5		9					-0.634923093	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_entropymax	0.1930000	0.0054772		0.1956000	0.0098641					0.2045556	0.0125111										4	5		9					-0.595254902	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_meanduration	0.0285000	0.0063509		0.0276000	0.0031305					0.0351000	0.0073401										4	5		10					-0.363987192	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_peakfrequstart	69339.9377500	6362.5905607		66031.8856000	3929.1367989					54773.7979000	4196.1911989										4	5		10					-0.321611224	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_minfreqstart	68133.1495000	6257.9552643		64796.1706000	4082.6666572					53275.0191000	4213.6700004										4	5		10					-0.305920148	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_maxfrequstart	71765.7477500	6142.9162222		68733.4816000	3952.8608450					57315.1191000	4048.9568152										4	5		10					-0.338502181	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_bandwstart	3589.9292500	176.6865194		3887.3656000	476.5605623					3994.0754000	380.9553189										4	5		10					-0.520505343	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_entropystart	0.1867500	0.0066521		0.1906000	0.0158524					0.2022000	0.0143279										4	5		10					-0.562390082	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_peakfrequend	80755.2170000	7130.3793512		78555.2896000	5371.4415642					66458.1019000	4740.6017565										4	5		10					-0.384697922	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_minfreqend	79211.8740000	7021.7575623		76984.6620000	5382.1778808					64829.2506000	4552.7376009										4	5		10					-0.376198029	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_maxfrequend	83357.7775000	7015.1036046		81298.9990000	5595.8063179					69166.6400000	4648.9534970										4	5		10					-0.397370659	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_bandwend	4100.5467500	191.5042202		4258.5490000	361.0614413					4288.1656000	429.1656564										4	5		10					-0.615910971	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_entropyend	0.1982500	0.0082614		0.2010000	0.0125499					0.2114000	0.0152184										4	5		10					-0.585899621	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_peakfrequcentre	74106.6220000	7044.5184840		73891.5844000	5092.2358605					61283.1031000	3445.7123977										4	5		10					-0.392168430	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_minfreqcenter	72668.0320000	6267.5002741		71986.6968000	4961.9873412					59578.0253000	3392.0761680										4	5		10					-0.378900511	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_maxfreqcenter	78169.5640000	5725.8571623		76532.0698000	5240.1316395					63854.9729000	3631.4868123										4	5		10					-0.373276772	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_bandwcentre	5460.9155000	1647.2090987		4501.4542000	316.2865754					4241.4512000	460.9409794										4	5		10					-0.295577798	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_entropycentre	0.2055000	0.0124499		0.2020000	0.0077782					0.2016000	0.0121033										4	5		10					-0.655308316	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_peakfrequmax	77823.1835000	5399.8126400		75780.1022000	5415.3370781					63366.8131000	3563.3066643										4	5		10					-0.368296771	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_minfreqmax	75957.8212500	5436.7383936		74190.8194000	5410.9982527					61786.7749000	3572.0160930										4	5		10					-0.366772281	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_maxfrequmax	80373.0087500	5426.3663453		78304.8670000	5489.1644712					65856.5116000	3558.8589776										4	5		10					-0.377998932	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_bandwmax	4377.1182500	458.3771312		4075.2264000	103.6274969					4038.5590000	95.8375937										4	5		10					-0.560824944	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_entropymax	0.1977500	0.0121758		0.1922000	0.0046583					0.1960000	0.0109747										4	5		10					-0.671129264	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_meanduration	0.0267500	0.0025000		0.0268000	0.0038987					0.0344000	0.0176522										4	5		10					-0.297408999	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_peakfrequstart	92946.9280000	15749.6397527		86481.4286000	5235.4356333					74913.1969000	7786.3062625										4	5		10					-0.352586813	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_minfreqstart	91265.4315000	15635.4785025		85101.1546000	5126.9975016					72762.6818000	8026.5298721										4	5		10					-0.335843653	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_maxfrequstart	95250.4370000	15611.6348854		88932.3952000	5456.0298891					77138.1818000	7532.7019771										4	5		10					-0.359960658	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_bandwstart	3938.6365000	407.4663826		3770.7360000	571.0050616					4327.9697000	1129.8378295										4	5		10					-0.539628821	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_entropystart	0.1940000	0.0162686		0.1890000	0.0151162					0.2120000	0.0258844										4	5		10					-0.548155483	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_peakfrequend	80321.3132500	13264.1601325		76316.7246000	3882.0213419					66018.2273000	6019.6800990										4	5		10					-0.382767555	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_minfreqend	78858.0547500	13220.5889301		74963.2754000	4014.2011731					64436.2727000	5953.7214161										4	5		10					-0.373728310	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_maxfrequend	83015.5642500	13098.4743972		79279.4950000	3972.5831489					68731.5455000	5833.3199357										4	5		10					-0.393987321	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_bandwend	4104.4317500	319.2320090		4266.2626000	160.5837063					4239.1515000	376.5780209										4	5		10					-0.635076378	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_entropyend	0.1957500	0.0127377		0.1992000	0.0032711					0.2177000	0.0218228										4	5		10					-0.521121428	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_peakfrequcentre	85783.7080000	13616.0068270		80867.3596000	5707.2122076					65722.3939000	5496.8625711										4	5		10					-0.274509367	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_minfreqcenter	84079.9412500	13437.8479318		79064.9062000	5758.6121464					64018.1667000	5500.6339410										4	5		10					-0.264941612	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_maxfreqcenter	88618.9480000	13627.6886529		83648.0088000	5894.4675948					68351.4394000	5563.6384045										4	5		10					-0.284838893	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_bandwcentre	4503.1745000	390.7571406		4530.2886000	282.0421900					4283.8030000	426.1499071										4	5		10					-0.607897412	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_entropycentre	0.2027500	0.0092871		0.2036000	0.0094499					0.2030000	0.0185053										4	5		10					-0.682922581	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_peakfrequmax	86744.4337500	13629.4541780		80625.3968000	5025.9533091					66385.7122000	5290.0326237										4	5		10					-0.272822757	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_minfreqmax	84959.1095000	13504.3981075		79065.7430000	5016.1123507					64919.6969000	5355.5238651										4	5		10					-0.270459052	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_maxfrequmax	89437.1925000	13622.5958524		83594.9494000	5076.7807417					68886.5151000	5319.5671675										4	5		10					-0.282693138	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_bandwmax	4434.5437500	323.8048150		4497.0850000	312.9490788					3952.0908000	142.0981267										4	5		10					-0.507412501	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_entropymax	0.1990000	0.0115181		0.2004000	0.0069498					0.1904000	0.0112270										4	5		10					-0.616775096	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_FAC2_1	2.0886949	1.1136315		1.5887848	0.3969399					0.8389634	0.6739541										4	5		10					0.720101036	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_meanduration	0.0265000	0.0106615		0.0292000	0.0046043					0.0490000	0.0188797										4	5		10					0.261944974	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_peakfrequstart	69169.0910000	9324.4840586		72231.8094000	4372.7725516					58622.4861000	4177.5985187										4	5		10					-0.430005096	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_minfreqstart	67626.3637500	9012.4152325		70881.4066000	4153.6520615					57128.3611000	4289.6169090										4	5		10					-0.424981568	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_maxfrequstart	71406.1362500	8903.2731162		74543.2748000	4406.7481866					60936.9862000	4080.3940669										4	5		10					-0.440621038	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_bandwstart	3756.6667500	157.6212831		3607.1428000	355.7839191					3761.9722000	634.3257829										4	5		10					-0.682646883	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_entropystart	0.1860000	0.0040825		0.1856000	0.0107842					0.2004000	0.0206839										4	5		10					-0.569964796	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_peakfrequend	69355.7577500	7543.9312104		73531.9192000	2979.5723937					59648.3194000	4627.5588424										4	5		10					-0.452573981	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_minfreqend	67679.9242500	7694.1337933		72042.2784000	2956.8202038					58407.7222000	4528.6555691										4	5		10					-0.457875137	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_maxfrequend	72289.7727500	7207.9161126		75923.6484000	3046.3029627					62455.1249000	4470.2205884										4	5		10					-0.459580398	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_bandwend	4533.4850000	626.2282663		3833.2748000	260.5736903					4000.4722000	271.3453083										4	5		10					-0.492166034	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_entropyend	0.2062500	0.0107819		0.1920000	0.0083964					0.2032000	0.0143201										4	5		10					-0.661873231	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_peakfrequcentre	68660.6817500	7663.4948537		72021.6116000	3615.6610863					59662.3749000	4149.6050515										4	5		10					-0.468451168	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_minfreqcenter	67142.2727500	7703.6470736		70267.8316000	3511.0302351					58124.8194000	4183.0463575										4	5		10					-0.462681393	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_maxfreqcenter	71632.0455000	7409.1498507		74567.8900000	3563.3700105					62026.3194000	4196.2300849										4	5		10					-0.463046975	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_bandwcentre	4444.6212500	673.9336392		4261.2308000	595.5497093					3878.5416000	177.2936857										4	5		10					-0.474981416	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_entropycentre	0.1990000	0.0134412		0.2014000	0.0173436					0.1961000	0.0112195										4	5		10					-0.662209346	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_peakfrequmax	68920.4545000	7723.1945601		73048.2564000	3656.6152656					60169.5138000	4456.3801999										4	5		10					-0.475671847	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_minfreqmax	67252.9545000	7730.3144970		71570.0880000	3681.7389827					58666.8750000	4456.7669770										4	5		10					-0.474441949	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_maxfrequmax	71630.7577500	7551.5296524		75444.2492000	3592.5206245					62560.9861000	4458.4226262										4	5		10					-0.476294880	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_bandwmax	4330.1515000	407.1581403		3852.3444000	191.5057684					3868.9027000	174.3981447										4	5		10					-0.511337687	Inviable
s19	Musolf et al_2015	S2	musculus.1	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_entropymax	0.1997500	0.0156498		0.1892000	0.0093381					0.1915000	0.0102008										4	5		10					-0.619853979	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_meanduration	0.0350000	0.0120277		0.0381667	0.0066458					0.0425000	0.0275772										4	6		2					-0.385768178	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_peakfrequstart	88243.2360000	13841.1853318		73305.6713333	4769.9467214					66238.6365000	12885.4138334										4	6		2					-0.243020520	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_minfreqstart	86607.4352500	13818.4480691		71600.6366667	4899.4887991					65145.4545000	13795.0105409										4	6		2					-0.246208851	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_maxfrequstart	91795.0325000	12995.6534046		75512.6351667	4620.4944652					69802.2725000	13785.3684328										4	6		2					-0.262953915	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_entropystart	0.2057500	0.0209185		0.1780000	0.0144914					0.2025000	0.0035355										4	6		2					-0.660296545	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_peakfrequend	74352.4027500	5053.6317335		77592.3225000	10448.6713702					61843.1820000	5666.4963576										4	6		2					-0.401081351	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_minfreqend	72380.2272500	5077.6205582		75370.5631667	9716.7533828					59870.4545000	5062.2417932										4	6		2					-0.392554591	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_maxfrequend	77172.7707500	5578.8877045		79990.5866667	10512.6172950					65202.2725000	4451.5589212										4	6		2					-0.425204686	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_bandwend	4737.1212500	658.3624192		4577.4498333	867.7424719					5268.1820000	662.1093341										4	6		2					-0.521158274	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_entropyend	0.2087500	0.0171925		0.1993333	0.0194182					0.2220000	0.0028284										4	6		2					-0.590032462	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_peakfrequcentre	85535.6927500	9125.8577495		77685.0115000	5235.8011518					63693.1820000	40385.4393138										4	6		2					-0.230662785	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_minfreqcenter	80507.1970000	8382.7388396		75118.2870000	4715.1801229					67679.5455000	33050.8137127										4	6		2					-0.417487596	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_maxfreqcenter	90638.4525000	8209.6444716		85149.2475000	7210.7901272					96740.9090000	2886.2811874										4	6		2					-0.584460074	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_bandwcentre	10071.6775000	4429.0518530		9980.9800000	5931.1228099					29015.9090000	35872.8125297										4	6		2					0.944963973	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_entropycentre	0.2252500	0.0343256		0.2113333	0.0233467					0.1615000	0.0544472										4	6		2					-0.172364334	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_peakfrequmax	80739.4265000	5303.4742393		78668.1135000	5702.1882220					70011.3635000	12075.4555425										4	6		2					-0.465225358	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_minfreqmax	79202.7922500	5361.1342413		76413.0593333	6057.2386935					68222.7275000	12554.3591664										4	6		2					-0.454960469	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_maxfrequmax	84045.2057500	4837.5806504		81311.1883333	5595.5060910					72500.0000000	12020.8152802										4	6		2					-0.457217796	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_bandwmax	4802.3810000	1332.0316481		4874.2476667	1278.9380970					4263.6365000	514.2596701										4	6		2					-0.501544967	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_entropymax	0.1950000	0.0107393		0.1935000	0.0051284					0.1985000	0.0049497										4	6		2					-0.657419957	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_FAC2_1	1.9098486	1.8018551		0.0221902	0.5561929					0.0761514	1.0631389										4	6		2					4.278011534	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Complex 2_FAC1_3	1.4977934	0.9941282		0.9259184	1.1546713					2.3807529	3.0564478										4	6		2					0.028976672	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_meanduration	0.0260000	0.0116619		0.0317143	0.0024300					0.0331000	0.0117988										4	7		10					-0.312943144	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_peakfrequstart	80479.7617500	12733.6564666		77879.0675714	4602.9590631					69479.9405000	11645.2813142										4	7		10					-0.458450927	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_minfreqstart	79164.8807500	12512.0095023		76067.6807143	4701.9548514					67727.2024000	11534.8704547										4	7		10					-0.444469588	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_maxfrequstart	82837.5000000	12669.1982077		80050.3137143	4775.5257050					71814.9405000	11778.6104092										4	7		10					-0.464888239	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_bandwstart	3639.8810000	853.3854609		3939.8922857	433.8022877					4029.5834000	680.4207605										4	7		10					-0.528156800	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_entropystart	0.1835000	0.0233880		0.1830000	0.0123018					0.1937000	0.0344353										4	7		10					-0.601498400	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_peakfrequend	79257.1427500	12462.3047588		81017.5178571	6016.4509795					71188.0953000	14556.4775881										4	7		10					-0.519438990	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_minfreqend	77598.8095000	12226.8021838		79011.1092857	5757.6575979					69773.2143000	14066.5281763										4	7		10					-0.521087133	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_maxfrequend	81726.7857500	12753.0766460		83337.7177143	6083.9637972					73858.9881000	14204.2004101										4	7		10					-0.528908317	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_bandwend	4083.3335000	786.2241599		4277.4301429	683.3155854					4027.2619000	688.5740425										4	7		10					-0.663523377	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_entropyend	0.1977500	0.0196193		0.1867143	0.0146483					0.2027000	0.0311450										4	7		10					-0.646739879	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_peakfrequcentre	74933.3332500	10968.5408399		70775.6034286	3871.4823805					65815.0594000	11251.9001331										4	7		10					-0.484970523	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_minfreqcenter	72616.6667500	12624.6233365		69188.4248571	3898.1055085					64420.1786000	11199.3646633										4	7		10					-0.500346412	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_maxfreqcenter	80083.9285000	8479.2792799		73324.4728571	4012.8599083					68504.0477000	10935.3646195										4	7		10					-0.444257818	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_bandwcentre	7431.5477500	6279.7280238		4103.1570000	295.9083431					4036.2500000	473.3382165										4	7		10					0.255387341	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_entropycentre	0.1990000	0.0053541		0.1841429	0.0055806					0.1986000	0.0183012										4	7		10					-0.681721500	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_peakfrequmax	79097.6192500	11822.7651521		74312.5190000	3267.9708291					69627.7381000	11480.9355266										4	7		10					-0.488405934	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_minfreqmax	77244.0475000	11659.2282489		72715.0254286	3256.1742969					68056.9047000	11431.6737156										4	7		10					-0.489783064	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_maxfrequmax	81376.1905000	11690.8610379		76883.3712857	3125.7538591					72044.1071000	11604.9769219										4	7		10					-0.497209421	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_bandwmax	4088.6907500	268.9063550		4128.2158571	202.3831040					3950.5357000	342.2510779										4	7		10					-0.631876020	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	U Shaped_entropymax	0.1930000	0.0054772		0.1828571	0.0047759					0.1946000	0.0209242										4	7		10					-0.672104203	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_meanduration	0.0285000	0.0063509		0.0300000	0.0036968					0.0274000	0.0056214										4	7		10					-0.624193998	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_peakfrequstart	69339.9377500	6362.5905607		68451.6210000	2875.9266220					62809.4284000	7177.9005225										4	7		10					-0.532463639	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_minfreqstart	68133.1495000	6257.9552643		67045.1720000	2746.4746641					61456.5655000	7116.6283122										4	7		10					-0.525967783	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_maxfrequstart	71765.7477500	6142.9162222		70854.8157143	2947.6970887					65648.8367000	7248.4481801										4	7		10					-0.547602042	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_bandwstart	3589.9292500	176.6865194		3775.6927143	345.8361665					4137.3085000	395.3846050										4	7		10					-0.466236524	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_entropystart	0.1867500	0.0066521		0.1787143	0.0085968					0.2046000	0.0203372										4	7		10					-0.544215608	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_peakfrequend	80755.2170000	7130.3793512		84010.6845714	7798.9189014					72124.2288000	8826.0091312										4	7		10					-0.510720144	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_minfreqend	79211.8740000	7021.7575623		82066.5910000	7545.2449096					70563.0160000	8746.6720876										4	7		10					-0.506734664	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_maxfrequend	83357.7775000	7015.1036046		86438.1841429	7823.3391056					74765.7748000	8842.4361599										4	7		10					-0.517267585	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_bandwend	4100.5467500	191.5042202		4319.4790000	348.1816172					4153.6997000	370.0441849										4	7		10					-0.664983337	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_entropyend	0.1982500	0.0082614		0.1891429	0.0079042					0.2075000	0.0245956										4	7		10					-0.614580556	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_peakfrequcentre	74106.6220000	7044.5184840		72854.8815714	4026.8954597					70103.3933000	7576.5492005										4	7		10					-0.599283815	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_minfreqcenter	72668.0320000	6267.5002741		70949.8125714	3823.1241531					68409.5260000	7641.2047793										4	7		10					-0.591804601	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_maxfreqcenter	78169.5640000	5725.8571623		75534.1190000	4229.4249557					72564.2708000	7651.9750334										4	7		10					-0.570212770	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_bandwcentre	5460.9155000	1647.2090987		4543.9688571	473.1665573					4121.6782000	165.3409998										4	7		10					-0.251456699	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_entropycentre	0.2055000	0.0124499		0.1920000	0.0080416					0.1996000	0.0182160										4	7		10					-0.639951570	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_peakfrequmax	77823.1835000	5399.8126400		75226.5405714	3353.0057009					70628.7162000	7823.2305707										4	7		10					-0.535410602	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_minfreqmax	75957.8212500	5436.7383936		73565.3675714	3374.9969637					69142.0198000	7795.7957098										4	7		10					-0.540011404	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_maxfrequmax	80373.0087500	5426.3663453		77772.7288571	3462.0600997					73062.2498000	7849.4368575										4	7		10					-0.537930443	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_bandwmax	4377.1182500	458.3771312		4169.4832857	128.2774426					3893.3592000	143.5696943										4	7		10					-0.504427057	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_entropymax	0.1977500	0.0121758		0.1842857	0.0035923					0.1923000	0.0179075										4	7		10					-0.641774811	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Upsweep_FAC1_1	1.0730254	0.6319905		1.1317147	0.6998538					0.3490031	0.8701206										4	7		10					1.045142241	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_meanduration	0.0267500	0.0025000		0.0188571	0.0048107					0.0342000	0.0090529										4	7		10					-0.306390184	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_peakfrequstart	92946.9280000	15749.6397527		83172.8458571	6591.5365310					74608.2984000	9917.5386682										4	7		10					-0.346305090	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_minfreqstart	91265.4315000	15635.4785025		81643.7951429	6791.6267628					73061.0328000	9813.6525389										4	7		10					-0.342146350	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_maxfrequstart	95250.4370000	15611.6348854		85390.5380000	6887.5176862					76974.2429000	9878.5598701										4	7		10					-0.356683699	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_bandwstart	3938.6365000	407.4663826		3697.4025714	538.0658783					3869.6487000	469.9430250										4	7		10					-0.657602743	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_entropystart	0.1940000	0.0162686		0.1780000	0.0167929					0.1974000	0.0245728										4	7		10					-0.658060052	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_peakfrequend	80321.3132500	13264.1601325		75947.5367143	5476.7229011					66474.0521000	8898.1472848										4	7		10					-0.393365840	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_minfreqend	78858.0547500	13220.5889301		74498.0982857	5701.1827887					64969.0043000	8816.5109640										4	7		10					-0.386410243	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_maxfrequend	83015.5642500	13098.4743972		78965.3731429	6242.7990745					69391.8936000	8906.2629017										4	7		10					-0.408715036	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_bandwend	4104.4317500	319.2320090		4429.1331429	1120.8845735					4364.9190000	441.7075033										4	7		10					-0.590044307	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_entropyend	0.1957500	0.0127377		0.2000000	0.0380701					0.2101000	0.0247855										4	7		10					-0.575853895	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_peakfrequcentre	85783.7080000	13616.0068270		77128.8085714	6248.6809489					69161.7468000	8267.9554886										4	7		10					-0.353075706	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_minfreqcenter	84079.9412500	13437.8479318		75565.8524286	6160.4471028					67649.9982000	8204.8806697										4	7		10					-0.349934308	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_maxfreqcenter	88618.9480000	13627.6886529		79822.0831429	6243.4272218					71784.9698000	8317.2169852										4	7		10					-0.360331315	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_bandwcentre	4503.1745000	390.7571406		4201.2110000	195.3805990					4089.9495000	253.4433097										4	7		10					-0.536569806	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_entropycentre	0.2027500	0.0092871		0.1898571	0.0052418					0.1996000	0.0182221										4	7		10					-0.660702597	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_peakfrequmax	86744.4337500	13629.4541780		77734.9567143	5425.7783009					70111.6135000	8434.1731776										4	7		10					-0.356931593	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_minfreqmax	84959.1095000	13504.3981075		76064.7290000	5169.3925630					68609.6506000	8448.2002277										4	7		10					-0.355608394	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_maxfrequmax	89437.1925000	13622.5958524		80257.0294286	5557.8365151					72587.0407000	8452.0702898										4	7		10					-0.363289184	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_bandwmax	4434.5437500	323.8048150		4156.9417143	513.0828757					3949.5721000	94.2193667										4	7		10					-0.506430562	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_entropymax	0.1990000	0.0115181		0.1850000	0.0129615					0.1937000	0.0171727										4	7		10					-0.643242225	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_FAC2_1	2.0886949	1.1136315		1.2441253	0.5051707					0.7618315	0.6656533										4	7		10					0.868647093	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Frequ Modulated Downsweep_FAC1_2	0.0126201	0.5416632		0.4169407	0.6986566					0.2071921	1.1713853										4	7		10					3.625400558	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_meanduration	0.0265000	0.0106615		0.0270000	0.0071880					0.0310000	0.0078031										4	7		10					-0.443241065	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_peakfrequstart	69169.0910000	9324.4840586		63660.0515301	25465.5528228					66991.5600000	8194.9523706										4	7		10					-0.635551346	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_minfreqstart	67626.3637500	9012.4152325		62489.4885774	24999.7828272					65844.5850000	8115.5487360										4	7		10					-0.643694361	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_maxfrequstart	71406.1362500	8903.2731162		65671.9343991	26271.1332349					69454.7217000	8199.8533693										4	7		10					-0.642141704	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_bandwstart	3756.6667500	157.6212831		3144.0522479	1261.7363913					3561.5091000	715.7415371										4	7		10					-0.602650831	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_entropystart	0.1860000	0.0040825		0.4902256	1.2484114					0.1876000	0.0312915										4	7		10					-0.671627672	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_peakfrequend	69355.7577500	7543.9312104		64144.7233062	25832.1297413					67522.4211000	8547.3791192										4	7		10					-0.643557662	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_minfreqend	67679.9242500	7694.1337933		62735.7946403	25225.2407203					66277.7084000	8406.3181878										4	7		10					-0.652573598	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_maxfrequend	72289.7727500	7207.9161126		66204.7049575	26631.2674949					70072.8344000	8466.4939927										4	7		10					-0.636845155	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_bandwend	4533.4850000	626.2282663		3436.0465660	1511.7179335					3743.4125000	411.9136739										4	7		10					-0.389869426	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_entropyend	0.2062500	0.0107819		0.4967598	1.2467428					0.1918000	0.0224935										4	7		10					-0.572941878	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_peakfrequcentre	68660.6817500	7663.4948537		63366.9744458	25500.1528586					68809.8620000	8155.6970594										4	7		10					-0.681477695	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_minfreqcenter	67142.2727500	7703.6470736		61936.1854557	24915.9881117					67349.2843000	8118.6006467										4	7		10					-0.680079017	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_maxfreqcenter	71632.0455000	7409.1498507		65518.2060007	26340.0697982					71150.9535000	8127.7818884										4	7		10					-0.674441053	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_bandwcentre	4444.6212500	673.9336392		3594.6877006	1395.2722123					3784.8907000	131.2380772										4	7		10					-0.437333862	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_entropycentre	0.1990000	0.0134412		0.4995162	1.2458998					0.1901000	0.0166897										4	7		10					-0.614346287	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_peakfrequmax	68920.4545000	7723.1945601		63978.4102444	25657.4176630					68911.7817000	8343.3168913										4	7		10					-0.684626797	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_minfreqmax	67252.9545000	7730.3144970		62686.6273322	25145.8487055					67480.7775000	8341.8147142										4	7		10					-0.679611707	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_maxfrequmax	71630.7577500	7551.5296524		66036.0423216	26494.0638733					71293.2103000	8344.5429680										4	7		10					-0.677545229	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_bandwmax	4330.1515000	407.1581403		3352.2286112	1328.7828084					3787.5374000	147.5658605										4	7		10					-0.478599456	Inviable
s19	Musolf et al_2015	S2	musculus.3	Mus	Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	Yes	sound	Constant Modulated_entropymax	0.1997500	0.0156498		0.4928359	1.2476990					0.1878000	0.0161644										4	7		10					-0.589802962	Inviable
s20	Paallysaho et al_2003	table1	Drosophila_montana_Drosophila_virilis	Drosophila	Drosophila_montana	Drosophila_virilis	Drosophila_montana_Drosophila_virilis	Yes	sound	PI	20.7000000	2.7100000					20.0000000	2.7400000		19.7000000	1.2500000						10	10			10		5	10					-0.608554123	Inviable
s20	Paallysaho et al_2003	table1	Drosophila_montana_Drosophila_virilis	Drosophila	Drosophila_montana	Drosophila_virilis	Drosophila_montana_Drosophila_virilis	Yes	sound	IPI	35.7000000	2.1100000					30.2000000	3.2700000		19.7000000	1.2500000						10	10			10		5	10					0.230918551	Inviable
s20	Paallysaho et al_2003	table1	Drosophila_littoralis_Drosophila_virilis	Drosophila	Drosophila_littoralis	Drosophila_virilis	Drosophila_littoralis_Drosophila_virilis	Yes	sound	PI	41.2000000	4.9600000					19.4000000	2.9000000		19.7000000	1.2500000						10	10			10		10	10					0.451619923	Inviable
s20	Paallysaho et al_2003	table1	Drosophila_littoralis_Drosophila_virilis	Drosophila	Drosophila_littoralis	Drosophila_virilis	Drosophila_littoralis_Drosophila_virilis	Yes	sound	IPI	286.0000000	51.5000000					39.4000000	2.3200000		19.7000000	1.2500000						10	10			10		10	10					3.435973249	Inviable
s20	Paallysaho et al_2003	table1	Drosophila_flavomontana_Drosophila_virilis	Drosophila	Drosophila_flavomontana	Drosophila_virilis	Drosophila_flavomontana_Drosophila_virilis	Yes	sound	PI	27.1000000	2.3800000		22.9000000	2.1800000		17.5000000	2.0700000		19.7000000	1.2500000				10	10	10	10			10	10	10	10					-0.193605661	Viable
s20	Paallysaho et al_2003	table1	Drosophila_flavomontana_Drosophila_virilis	Drosophila	Drosophila_flavomontana	Drosophila_virilis	Drosophila_flavomontana_Drosophila_virilis	Yes	sound	IPI	113.0000000	8.9300000		42.6000000	5.2100000		29.5000000	2.2200000		19.7000000	1.2500000				10	10	10	10			10	10	10	10					2.005673474	Viable
s20	Paallysaho et al_2003	table1	Drosophila_flavomontana_Drosophila_montana	Drosophila	Drosophila_flavomontana	Drosophila_montana	Drosophila_flavomontana_Drosophila_montana	Yes	sound	pause	85.7000000	9.9400000		39.0000000	8.8800000					15.0000000	2.2100000				10	10					10	8		10					1.999563908	Inviable
s20	Paallysaho et al_2003	table1	Drosophila_flavomontana_Drosophila_montana	Drosophila	Drosophila_flavomontana	Drosophila_montana	Drosophila_flavomontana_Drosophila_montana	Yes	sound	PI	27.1000000	2.3800000		16.6000000	0.9200000					20.7000000	2.7100000				10	10					10	8		10					-0.269872170	Inviable
s20	Paallysaho et al_2003	table1	Drosophila_flavomontana_Drosophila_montana	Drosophila	Drosophila_flavomontana	Drosophila_montana	Drosophila_flavomontana_Drosophila_montana	Yes	sound	IPI	113.0000000	8.9300000		55.6000000	9.0200000					35.7000000	2.1100000				10	10					10	8		10					1.089934291	Inviable
s20	Paallysaho et al_2003	table1	Drosophila_flavomontana_Drosophila_littoralis	Drosophila	Drosophila_flavomontana	Drosophila_littoralis	Drosophila_flavomontana_Drosophila_littoralis	Yes	sound	pause	85.7000000	9.9400000		128.0000000	19.1000000					245.0000000	52.4000000				10	10					10	5		10					0.933086029	Inviable
s20	Paallysaho et al_2003	table1	Drosophila_flavomontana_Drosophila_littoralis	Drosophila	Drosophila_flavomontana	Drosophila_littoralis	Drosophila_flavomontana_Drosophila_littoralis	Yes	sound	PI	27.1000000	2.3800000		20.8000000	0.8400000					41.2000000	4.9600000				10	10					10	5		10					-0.039595049	Inviable
s20	Paallysaho et al_2003	table1	Drosophila_flavomontana_Drosophila_littoralis	Drosophila	Drosophila_flavomontana	Drosophila_littoralis	Drosophila_flavomontana_Drosophila_littoralis	Yes	sound	IPI	113.0000000	8.9300000		148.0000000	19.1000000					286.0000000	51.5000000				10	10					10	5		10					0.745479142	Inviable
s22	Shaw_1996	table4	KW L. paranigra	Laupala	Laupala_kohalensis	Laupala_paranigra	Laupala_kohalensis_Laupala_paranigra	Yes	sound	Pulse period	0.2710000	0.0100000		0.4640000	0.0140000		0.5960000	0.0150000		1.4230000	0.1500000		40	40	5	5	2	2	28	28	40	5	2	28					1.869566995	Viable
s22	Shaw_1996	table4	KW L. paranigra	Laupala	Laupala_kohalensis	Laupala_paranigra	Laupala_kohalensis_Laupala_paranigra	Yes	sound	Pulse rate	3.6940000	0.1320000		2.1590000	0.0670000		1.6780000	0.0420000		0.7180000	0.0790000		40	40	5	5	2	2	28	28	40	5	2	28					1.838132903	Viable
s22	Shaw_1996	table4	KW L. paranigra	Laupala	Laupala_kohalensis	Laupala_paranigra	Laupala_kohalensis_Laupala_paranigra	Yes	sound	Carrier frequency	4.9270000	0.2460000		5.2930000	0.3040000		5.2930000	0.3040000		5.1180000	0.1990000		38	38	5	5	2	2	27	27	38	5	2	27					-0.626238889	Viable
s22	Shaw_1996	table4	KW L. paranigra	Laupala	Laupala_kohalensis	Laupala_paranigra	Laupala_kohalensis_Laupala_paranigra	Yes	sound	Pulse duration	39.7700000	4.4100000		38.4000000	2.3300000		38.7600000	0.3400000		38.3700000	4.5300000		37	37	5	5	2	2	27	27	37	5	2	27					-0.629622089	Viable
s22	Shaw_1996	table4	OF L. paranigra	Laupala	Laupala_kohalensis	Laupala_paranigra	Laupala_kohalensis_Laupala_paranigra	Yes	sound	Pulse period	0.2710000	0.0100000		0.5200000	0.0230000		0.5730000	0.0270000		1.0790000	0.0870000		40	40	10	10	4	4	27	27	40	10	4	27					1.443324350	Viable
s22	Shaw_1996	table4	OF L. paranigra	Laupala	Laupala_kohalensis	Laupala_paranigra	Laupala_kohalensis_Laupala_paranigra	Yes	sound	Pulse rate	3.6940000	0.1320000		1.9250000	0.0860000		1.7480000	0.0800000		0.9330000	0.0780000		40	40	10	10	4	4	27	27	40	10	4	27					1.434681617	Viable
s22	Shaw_1996	table4	OF L. paranigra	Laupala	Laupala_kohalensis	Laupala_paranigra	Laupala_kohalensis_Laupala_paranigra	Yes	sound	Carrier frequency	4.9270000	0.2460000		4.9940000	0.0960000		5.0190000	0.2450000		5.0810000	0.2330000		38	38	10	10	4	4	25	25	38	10	4	25					-0.637414533	Viable
s22	Shaw_1996	table4	OF L. paranigra	Laupala	Laupala_kohalensis	Laupala_paranigra	Laupala_kohalensis_Laupala_paranigra	Yes	sound	Pulse duration	39.7700000	4.4100000		39.9600000	1.8500000		35.9400000	3.3600000		38.1700000	0.6000000		37	37	10	10	4	4	25	25	37	10	4	25					-0.621572586	Viable
s25	Suvanto et al_1994	table 1&2	Drosophila_lummei_Drosophila_virilis	Drosophila	Drosophila_lummei	Drosophila_virilis	Drosophila_lummei_Drosophila_virilis	Yes	sound	inhbt_PL	9.0000000	3.5774006		10.0000000	3.1841727		11.0000000	8.3708755		11.0000000	3.7425055										5	5	4	5	105	104	87	112	-0.375733818	Viable
s25	Suvanto et al_1994	table 1&2	Drosophila_lummei_Drosophila_virilis	Drosophila	Drosophila_lummei	Drosophila_virilis	Drosophila_lummei_Drosophila_virilis	Yes	sound	inhbt_CN	3.0000000	0.9937224		3.0000000	0.9950540		3.0000000	3.2666831		4.0000000	0.9848699										5	5	4	5	105	104	87	112	-0.241712907	Viable
s25	Suvanto et al_1994	table 1&2	Drosophila_lummei_Drosophila_virilis	Drosophila	Drosophila_lummei	Drosophila_virilis	Drosophila_lummei_Drosophila_virilis	Yes	sound	inhbt_IPI	51.0000000	16.0983025		53.0000000	26.8664575		48.0000000	30.2168190		46.0000000	30.5309658										5	5	4	5	105	104	87	112	-0.525889171	Viable
s25	Suvanto et al_1994	table 1&2	Drosophila_borealis_Drosophila_virilis	Drosophila	Drosophila_borealis	Drosophila_virilis	Drosophila_borealis_Drosophila_virilis	Yes	sound	inhbt_PL	8.0000000	9.6613487		12.0000000	5.9309256		12.0000000	8.4409717		11.0000000	3.7425055										4	4	2	5	96	86	51	112	-0.194316281	Viable
s25	Suvanto et al_1994	table 1&2	Drosophila_borealis_Drosophila_virilis	Drosophila	Drosophila_borealis	Drosophila_virilis	Drosophila_borealis_Drosophila_virilis	Yes	sound	inhbt_CN	2.0000000	1.8115029		3.0000000	1.8406321		3.0000000	1.5549158		4.0000000	0.9848699										4	4	2	5	96	86	51	112	0.382812357	Viable
s25	Suvanto et al_1994	table 1&2	Drosophila_borealis_Drosophila_virilis	Drosophila	Drosophila_borealis	Drosophila_virilis	Drosophila_borealis_Drosophila_virilis	Yes	sound	inhbt_IPI	28.0000000	57.9680922		34.0000000	61.5589175		30.0000000	60.4195866		46.0000000	30.5309658										4	4	2	5	96	86	51	112	0.079825624	Viable
s25	Suvanto et al_1994	table 1&2	Drosophila_littoralis_Drosophila_virilis	Drosophila	Drosophila_littoralis	Drosophila_virilis	Drosophila_littoralis_Drosophila_virilis	Yes	sound	inhbt_PL	9.0000000	5.0099491					11.0000000	4.9521372		11.0000000	3.7425055										5		4	5	99		81	112	-0.375733818	Inviable
s25	Suvanto et al_1994	table 1&2	Drosophila_littoralis_Drosophila_virilis	Drosophila	Drosophila_littoralis	Drosophila_virilis	Drosophila_littoralis_Drosophila_virilis	Yes	sound	inhbt_CN	2.0000000	1.0019898					3.0000000	1.2380343		4.0000000	0.9848699										5		4	5	99		81	112	0.382812357	Inviable
s25	Suvanto et al_1994	table 1&2	Drosophila_littoralis_Drosophila_virilis	Drosophila	Drosophila_littoralis	Drosophila_virilis	Drosophila_littoralis_Drosophila_virilis	Yes	sound	inhbt_IPI	62.0000000	40.6807864					34.0000000	35.4903165		46.0000000	30.5309658										5		4	5	99		81	112	-0.225061190	Inviable
s25	Suvanto et al_1994	table 1&2	Drosophila_montana_Drosophila_virilis	Drosophila	Drosophila_montana	Drosophila_virilis	Drosophila_montana_Drosophila_virilis	Yes	sound	inhbt_PL	12.0000000	41.0676782					13.0000000	8.4855081		11.0000000	3.7425055										5		5	5	114		131	112	-0.550799722	Inviable
s25	Suvanto et al_1994	table 1&2	Drosophila_montana_Drosophila_virilis	Drosophila	Drosophila_montana	Drosophila_virilis	Drosophila_montana_Drosophila_virilis	Yes	sound	inhbt_CN	3.0000000	4.9124017					4.0000000	1.3499672		4.0000000	0.9848699										5		5	5	114		131	112	-0.241712907	Inviable
s25	Suvanto et al_1994	table 1&2	Drosophila_montana_Drosophila_virilis	Drosophila	Drosophila_montana	Drosophila_virilis	Drosophila_montana_Drosophila_virilis	Yes	sound	inhbt_IPI	21.0000000	21.8110635					35.0000000	20.8280654		46.0000000	30.5309658										5		5	5	114		131	112	0.522933350	Inviable
s25	Suvanto et al_1994	table 1&2	Drosophila_kanekoi_Drosophila_virilis	Drosophila	Drosophila_kanekoi	Drosophila_virilis	Drosophila_kanekoi_Drosophila_virilis	Yes	sound	inhbt_PL	25.0000000	17.1493686		11.0000000	3.5013480					11.0000000	3.7425055										4	3		5	28	82		112	0.579710113	Inviable
s25	Suvanto et al_1994	table 1&2	Drosophila_kanekoi_Drosophila_virilis	Drosophila	Drosophila_kanekoi	Drosophila_virilis	Drosophila_kanekoi_Drosophila_virilis	Yes	sound	inhbt_CN	5.0000000	3.7281236		4.0000000	1.0298082					4.0000000	0.9848699										4	3		5	28	82		112	-0.341119579	Inviable
s25	Suvanto et al_1994	table 1&2	Drosophila_kanekoi_Drosophila_virilis	Drosophila	Drosophila_kanekoi	Drosophila_virilis	Drosophila_kanekoi_Drosophila_virilis	Yes	sound	inhbt_IPI	218.0000000	92.2089242		36.0000000	23.6855892					46.0000000	30.5309658										4	3		5	28	82		112	1.711612257	Inviable
s25	Suvanto et al_1994	table 1&2	Drosophila_flavomontana_Drosophila_lacicola	Drosophila	Drosophila_flavomontana	Drosophila_lacicola	Drosophila_flavomontana_Drosophila_lacicola	Yes	sound	inhbt_PL	13.0000000	6.8200006		12.0000000	3.9527756					11.0000000	4.6213692										5	5		5	187	158		72	-0.427512435	Inviable
s25	Suvanto et al_1994	table 1&2	Drosophila_flavomontana_Drosophila_lacicola	Drosophila	Drosophila_flavomontana	Drosophila_lacicola	Drosophila_flavomontana_Drosophila_lacicola	Yes	sound	inhbt_CN	3.0000000	1.1059460		3.0000000	0.9411370					3.0000000	1.0503112										5	5		5	187	158		72	-0.684820633	Inviable
s25	Suvanto et al_1994	table 1&2	Drosophila_flavomontana_Drosophila_lacicola	Drosophila	Drosophila_flavomontana	Drosophila_lacicola	Drosophila_flavomontana_Drosophila_lacicola	Yes	sound	inhbt_IPI	31.0000000	15.8518933		35.0000000	15.0581927					62.0000000	38.0212646										5	5		5	187	158		72	0.382812357	Inviable
s26	Tomaru&Oguma_1994	table1	A541.auraria_B16.biauraria	Drosophila	Drosophila_auraria	Drosophila_biauraria	Drosophila_auraria_Drosophila_biauraria	Yes	sound	IPI	21.4000000	1.9000000		15.7000000	0.5200000		16.0000000	0.5000000		12.9000000	0.9100000		6	6	10~20	10~20	10~20	10~20	10	10	6	22	15	10	180	220	150	300	0.094807394	Viable
s26	Tomaru&Oguma_1994	table1	A541.auraria_B18.biauraria	Drosophila	Drosophila_auraria	Drosophila_biauraria	Drosophila_auraria_Drosophila_biauraria	Yes	sound	IPI	21.4000000	1.9000000		15.2000000	1.3100000		16.2000000	0.4200000		12.4000000	0.7600000		6	6	10~20	10~20	6	10~20	9	9	6	12	6	9	180	120	60	270	0.155695512	Viable
s26	Tomaru&Oguma_1994	table1	A662.auraria_B18.biauraria	Drosophila	Drosophila_auraria	Drosophila_biauraria	Drosophila_auraria_Drosophila_biauraria	Yes	sound	IPI	18.9000000	1.9800000		16.6000000	0.3700000		15.8000000	0.4900000		12.4000000	0.7600000		8	8	5	5	6	6	9	9	8	5	6	9	240	50	60	270	-0.035650543	Viable
s26	Tomaru&Oguma_1994	table1	sn.cn.ba2.auraria_B16.biauraria	Drosophila	Drosophila_auraria	Drosophila_biauraria	Drosophila_auraria_Drosophila_biauraria	Yes	sound	IPI	19.9000000	0.7200000					16.3000000	0.6000000		12.9000000	0.9100000						10~20	10~20	10	10	11		27	10	110		270	300	-0.017125774	Inviable
s26	Tomaru&Oguma_1994	table1	A541.auraria_T544_triauraria	Drosophila	Drosophila_auraria	Drosophila_triauraria	Drosophila_auraria_Drosophila_triauraria	Yes	sound	IPI	21.4000000	1.9000000		17.3000000	0.4300000		18.1000000	0.3900000		16.1000000	0.9600000		6	6	8	8	10	10	7	7	6	8	10	7	180	80	100	210	-0.246503794	Viable
s26	Tomaru&Oguma_1994	table1	A541.auraria_quadraria	Drosophila	Drosophila_auraria	Drosophila_quadraria	Drosophila_auraria_Drosophila_quadraria	Yes	sound	IPI	21.4000000	1.9000000		17.1000000	0.4100000		17.6000000	0.2600000		15.6000000	0.6800000		6	6	9	9	6	6	5	5	6	9	6	5	180	90	60	150	-0.197910842	Viable
s26	Tomaru&Oguma_1994	table1	A541.auraria_subauraria	Drosophila	Drosophila_auraria	Drosophila_subauraria	Drosophila_auraria_Drosophila_subauraria	Yes	sound	IPI	21.4000000	1.9000000		14.3000000	0.4000000					11.1000000	0.6400000		6	6	3	3			10	10	6	3		10	180	30		258	0.326282386	Inviable
s26	Tomaru&Oguma_1994	table1	Drosophila_triauraria_Drosophila_quadraria	Drosophila	Drosophila_triauraria	Drosophila_quadraria	Drosophila_triauraria_Drosophila_quadraria	Yes	sound	IPI	16.1000000	0.9600000		14.9000000	0.3700000		16.5000000	0.5900000		15.6000000	0.6800000		7	7	9	9	6	6	5	5	210	9	6	150					-0.636227681	Viable
s26	Tomaru&Oguma_1994	table1	Drosophila_triauraria_Drosophila_biauraria	Drosophila	Drosophila_triauraria	Drosophila_biauraria	Drosophila_triauraria_Drosophila_biauraria	Yes	sound	IPI	16.1000000	0.9600000					14.3000000	0.6000000		12.9000000	0.9100000		7	7			6	6	10	10	210		60	300					-0.343509445	Inviable
s26	Tomaru&Oguma_1994	table1	Drosophila_quadraria_Drosophila_biauraria	Drosophila	Drosophila_quadraria	Drosophila_biauraria	Drosophila_quadraria_Drosophila_biauraria	Yes	sound	IPI	11.1000000	0.6400000		13.4000000	0.2700000		13.9000000	0.2800000		12.9000000	0.9100000		5	5	10	10	5	5	10	10	150	10	5	300					-0.453345642	Viable
s26	Tomaru&Oguma_1994	table1	Drosophila_quadraria_Drosophila_subauraria	Drosophila	Drosophila_quadraria	Drosophila_subauraria	Drosophila_quadraria_Drosophila_subauraria	Yes	sound	IPI	11.1000000	0.6400000		13.3000000	0.5200000					11.1000000	0.6400000		5	5	10~20	10~20			10	10	150	140		258					-0.684820633	Inviable
s26	Tomaru&Oguma_1994	table1	Drosophila_biauraria_Drosophila_subauraria	Drosophila	Drosophila_biauraria	Drosophila_subauraria	Drosophila_biauraria_Drosophila_subauraria	Yes	sound	IPI	12.9000000	0.9100000		11.8000000	0.3600000		11.5000000	0.5100000		11.1000000	0.6400000		10	10	8	8	8	8	10	10	300	8	8	258					-0.453345642	Viable
s27	Wells_1994	table1	Chrysoperla_johnsoni_Chrysoperla_plorabunda	Chrysoperla	Chrysoperla_johnsoni	Chrysoperla_plorabunda	Chrysoperla_johnsoni_Chrysoperla_plorabunda	Yes	sound	Initial Volley frequency	71.3000000	4.9193496	1.100	82.2000000	4.4090815	0.900	79.3000000	2.5000000	0.500	75.1000000	7.6733304	1.6000	10	10	10	10	10	10	10	10	20	24	25	23					-0.604843358	Viable
s27	Wells_1994	table1	Chrysoperla_johnsoni_Chrysoperla_plorabunda	Chrysoperla	Chrysoperla_johnsoni	Chrysoperla_plorabunda	Chrysoperla_johnsoni_Chrysoperla_plorabunda	Yes	sound	Middle Volley frequency	58.2000000	2.2360680	0.500	67.9000000	2.9393877	0.600	64.3000000	3.5000000	0.700	59.1000000	3.3570821	0.7000	10	10	10	10	10	10	10	10	20	24	25	23					-0.661184331	Viable
s27	Wells_1994	table1	Chrysoperla_johnsoni_Chrysoperla_plorabunda	Chrysoperla	Chrysoperla_johnsoni	Chrysoperla_plorabunda	Chrysoperla_johnsoni_Chrysoperla_plorabunda	Yes	sound	End Volley frequency	46.2000000	4.0249224	0.900	43.5000000	6.3686733	1.300	37.3000000	5.5000000	1.100	40.3000000	3.3570821	0.7000	10	10	10	10	10	10	10	10	20	24	25	23					-0.474376278	Viable
s27	Wells_1994	table1	Chrysoperla_johnsoni_Chrysoperla_plorabunda	Chrysoperla	Chrysoperla_johnsoni	Chrysoperla_plorabunda	Chrysoperla_johnsoni_Chrysoperla_plorabunda	Yes	sound	I-E Volley frequency	25.1000000	7.6026311	1.700	38.7000000	7.3484692	1.500	41.9000000	4.5000000	0.900	34.7000000	8.6324967	1.8000	10	10	10	10	10	10	10	10	20	24	25	23					-0.185970935	Viable
s27	Wells_1994	table1	Chrysoperla_johnsoni_Chrysoperla_plorabunda	Chrysoperla	Chrysoperla_johnsoni	Chrysoperla_plorabunda	Chrysoperla_johnsoni_Chrysoperla_plorabunda	Yes	sound	Volley duration	1586.0000000	652.9318494	146.000	892.0000000	274.3428512	56.000	718.0000000	170.0000000	34.000	604.0000000	105.5082935	22.0000	10	10	10	10	10	10	10	10	20	24	25	23					0.802149039	Viable
s27	Wells_1994	table1	Chrysoperla_johnsoni_Chrysoperla_plorabunda	Chrysoperla	Chrysoperla_johnsoni	Chrysoperla_plorabunda	Chrysoperla_johnsoni_Chrysoperla_plorabunda	Yes	sound	Volley interval	3499.0000000	1350.5850584	302.000	2220.0000000	357.6255024	73.000	1938.0000000	335.0000000	67.000	1199.0000000	153.4666087	32.0000	10	10	10	10	10	10	10	10	20	24	25	23					0.964790849	Viable
s28	Shaw_2000	table1	KW L. paranigra	Laupala	Laupala_kohalensis	Laupala_paranigra	Laupala_kohalensis_Laupala_paranigra	Yes	sound	Pulse duration	3.7500000	0.1400000		2.2200000	0.0700000		1.8800000	0.1200000		0.9400000	0.0500000				8	8	4	4			10	4	2	10					1.446343429	Viable
s28	Shaw_2000	table1	SB L. paranigra	Laupala	Laupala_kohalensis	Laupala_paranigra	Laupala_kohalensis_Laupala_paranigra	Yes	sound	Pulse duration	3.7500000	0.1400000		2.4300000	0.0700000		2.0900000	0.1200000		1.0500000	0.0700000				8	8	4	4			10	2	2	10					1.275888706	Viable
s29	Sasabe_et.al_2007	fig2+text	Carabus_iwawakianus_Carabus_maiyasanus	Carabus	Carabus_iwawakianus	Carabus_maiyasanus	Carabus_iwawakianus_Carabus_maiyasanus	Yes	morphology	CPL	1.2740000	0.0650000		1.7780000	0.1470000		1.8940000	0.1120000		2.5360000	0.1410000				13	13	33	33			33	26	96	22					0.375541208	Viable
s29	Sasabe_et.al_2007	fig2+text	Carabus_iwawakianus_Carabus_maiyasanus	Carabus	Carabus_iwawakianus	Carabus_maiyasanus	Carabus_iwawakianus_Carabus_maiyasanus	Yes	morphology	CPW	0.7530000	0.0360000		0.4820000	0.0400000		0.4300000	0.0450000		0.3600000	0.0200000				13	13	33	33			33	26	96	22					0.451837988	Viable

Study.ID: ID of studies

Study.name: Including author name(s) and published year

Source: Source of phenotypc data in the primary studes

Cross.ID: Parental species used in the crossing expreiments. Distinguishes intraspecific populations.

Gennus: Studied genus

sp1.name and sp2.name: Crossed species. Ordered alphabetically.

species.pair: Parental species used in the crossing expreiments. Not distinguiscr intraspecific populations.

homologous: Measured traits are homologous across all crosses. If not, data has been excluded

trait.type: Sound or Morphology. Used in regressions bellow as a moderator.

trait: Trait name described in primary studies

Mn.~: Phenotypic mean of each cross

SD.~: SD of phenotype in each cross

SE.~: SE of phenotype in each cross

Parents.~: Number of parents used in each crossing. Usually not provided by the primary studies.

N.~: Number of individuals whose phenotype was measured

reps.~: Number of measurements, if replicated (especially in behavioral traits). Not used in the analyses bellow.

Pheno.divergence: Phenotypic divergence between parents in the focal trait, which is calculated as absolute value of log response ratio (lnRR) of phenotypic means. Used in regressions bellow as a moderator.

The species-level moderator dataset of this study.

meta <- read.xlsx(
    "../data/original.data.xlsx", sheet = "Species.level.moderators"
    # Same data is also saved as "../data/original.data.Species.level.moderators.txt"
    ) %>%
  mutate_at(vars(contains("divergence")), as.numeric) %>%
  # Logalize genetic divergence
  mutate_at(vars(contains("divergence")), log10)

# making a scrollable table
kable(meta, "html") %>% kable_styling("striped", position = "left") %>% 
    scroll_box(width = "100%", height = "500px")

sp1.name	sp2.name	species.pair	Cross.ID	Distribution	Source_distribution	taxa	souce_sex.determinaton	sex.determination	Hetero.sex	Genet.divergence	Survival.sp1	Survival.hyb12	Survival.hyb21	Survival.sp2	surv.relat.hyb12	surv.relat.hyb21	surv.File	surv.Source	surv.Study.name	comments	parentals
Drosophila_auraria	Drosophila_biauraria	Drosophila_auraria_Drosophila_biauraria	A541.auraria_B16.biauraria	Overlap	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-1.4814861					NA	NA				parental data provided in other paper (Tomaru.Oguma_1993.AnimBehav)_N: n of song_Parents: n of indv (mass culture)
Drosophila_auraria	Drosophila_biauraria	Drosophila_auraria_Drosophila_biauraria	A541.auraria_B18.biauraria	Overlap	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-1.4814861					NA	NA				parental data provided in other paper (Tomaru.Oguma_1993.AnimBehav)_N: n of song_Parents: n of indv (mass culture)
Drosophila_auraria	Drosophila_quadraria	Drosophila_auraria_Drosophila_quadraria	A541.auraria_quadraria	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-3.0000000					NA	NA
Drosophila_auraria	Drosophila_subauraria	Drosophila_auraria_Drosophila_subauraria	A541.auraria_subauraria	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-1.4814861					NA	NA
Drosophila_auraria	Drosophila_triauraria	Drosophila_auraria_Drosophila_triauraria	A541.auraria_T544_triauraria	Overlap	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-2.6989700					NA	NA
Drosophila_auraria	Drosophila_biauraria	Drosophila_auraria_Drosophila_biauraria	A662.auraria_B18.biauraria	Overlap	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-1.4814861					NA	NA				parental data provided in other paper (Tomaru.Oguma_1993.AnimBehav)_N: n of song_Parents: n of indv (mass culture)
Agrotis_ipsilon	Agrotis_segetum	Agrotis_ipsilon_Agrotis_segetum	Agrotis_ipsilon_Agrotis_segetum	Overlap	original study	Lepidoptera	Traut W. K. Sahara and F. Marec. “Sex chromosomes and sex determination in Lepidoptera.” Sexual Development 1.6 (2007): 332-346.	ZW/Z0	Female	-1.3098039	65	1	0	60	-4.1351665567423561	NA	Gadenne_PheromoneReceptor_JChemEcol.1997	table1	Gadenne et al_1997
Drosophila_melanogaster	Drosophila_simulans	Drosophila_melanogaster_Drosophila_simulans	Basc	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-1.2924298	1	0.5	0.2	1	-0.69314718055994529	-1.6094379124341003	reviewed in Sawamura_
Drosophila_mauritiana	Drosophila_simulans	Drosophila_mauritiana_Drosophila_simulans	BGxMutant	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-1.7212464					NA	NA
Drosophila_mauritiana	Drosophila_simulans	Drosophila_mauritiana_Drosophila_simulans	BGxOxnard	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-1.7212464					NA	NA
Drosophila_mauritiana	Drosophila_simulans	Drosophila_mauritiana_Drosophila_simulans	BGxSPD(wildtype)	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-1.7212464					NA	NA
Carabus_iwawakianus	Carabus_maiyasanus	Carabus_iwawakianus_Carabus_maiyasanus	Carabus_iwawakianus_Carabus_maiyasanus	Overlap	original study	Coleoptera	Serrano J. and J. S. Yadav. “Chromosome numbers and sex-determining mechanisms in adephagan Coleoptera.” The Coleopterists’ Bulletin (1984): 335-357.	XY	Male	-1.8538720
Drosophila_madeirensis	Drosophila_subobscura	Drosophila_madeirensis_Drosophila_subobscura	chcu	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	0.5435714					NA	NA
Chorthippus_biguttulus	Chorthippus_brunneus	Chorthippus_biguttulus_Chorthippus_brunneus	Chorthippus_biguttulus_Chorthippus_brunneus	Overlap	original study	Orthoptera	Castillo Elio Rodrigo Dardo Andrea Marti and Claudio Juan Bidau. “Sex and neo-sex chromosomes in Orthoptera: a review.” Journal of Orthoptera Research (2010): 213-231.	X0/XY	Male	-0.0710923	0.59322033898305082	0.5	0.72368421052631582	0.35294117647058826	5.5341989352007639E-2	0.42508901485809281	Perdeck_1958_Grasshopper	table7			wild but caught in juvenile stage
Chrysoperla_johnsoni	Chrysoperla_plorabunda	Chrysoperla_johnsoni_Chrysoperla_plorabunda	Chrysoperla_johnsoni_Chrysoperla_plorabunda	Overlap	original study	Neuroptera	Tauber Catherine A. and Maurice J. Tauber. “Inheritance of seasonal cycles in Chrysoperla (Insecta: Neuroptera).” Genetics Research 49.3 (1987): 215-223.	XY	Male	-1.9586073	0.62551999999999996	0.45435714285714285	0.489375	0.54733333333333334	-0.25516407769093707	-0.1809185580094152	Wells_1993_Lacewings	table1
Drosophila_biauraria	Drosophila_subauraria	Drosophila_biauraria_Drosophila_subauraria	Drosophila_biauraria_Drosophila_subauraria	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-1.9586073					NA	NA
Drosophila_borealis	Drosophila_virilis	Drosophila_borealis_Drosophila_virilis	Drosophila_borealis_Drosophila_virilis	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-0.9208188				0.99	NA	NA				Parents: ?? (mass culture)_SD estimated by Wan et al 2014
Drosophila_buzzatii	Drosophila_koepferae	Drosophila_buzzatii_Drosophila_koepferae	Drosophila_buzzatii_Drosophila_koepferae	Overlap	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	0.0224284					NA	NA
Drosophila_flavomontana	Drosophila_lacicola	Drosophila_flavomontana_Drosophila_lacicola	Drosophila_flavomontana_Drosophila_lacicola	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	0.0047512					NA	NA				Parents: ?? (mass culture)_SD estimated by Wan et al 2014
Drosophila_flavomontana	Drosophila_littoralis	Drosophila_flavomontana_Drosophila_littoralis	Drosophila_flavomontana_Drosophila_littoralis	Allopatry	original paper	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-0.1938200					NA	NA
Drosophila_flavomontana	Drosophila_montana	Drosophila_flavomontana_Drosophila_montana	Drosophila_flavomontana_Drosophila_montana	Allopatry	original paper	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	0.1048284					NA	NA
Drosophila_flavomontana	Drosophila_virilis	Drosophila_flavomontana_Drosophila_virilis	Drosophila_flavomontana_Drosophila_virilis	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	0.0132587				0.99	NA	NA
Drosophila_heteroneura	Drosophila_silvestris	Drosophila_heteroneura_Drosophila_silvestris	Drosophila_heteroneura_Drosophila_silvestris	Overlap	“Drosophila heteroneura and Drosophila silvestris: Head Shapes, Behavior and Evolution”	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-2.0457575					NA	NA
Drosophila_kanekoi	Drosophila_virilis	Drosophila_kanekoi_Drosophila_virilis	Drosophila_kanekoi_Drosophila_virilis	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-0.9507820				0.99	NA	NA				Parents: ?? (mass culture)_SD estimated by Wan et al 2014
Drosophila_littoralis	Drosophila_virilis	Drosophila_littoralis_Drosophila_virilis	Drosophila_littoralis_Drosophila_virilis	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-0.9136402	0.95499999999999996	0	0.16	0.99	0	-1.8046962602587744	Orr_Coyne_1989_Drosophila	fertility_table2&3
Drosophila_lummei	Drosophila_virilis	Drosophila_lummei_Drosophila_virilis	Drosophila_lummei_Drosophila_virilis	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-1.2441251	0.97499999999999998	0.98	0.95	0.99	-2.5477720787986644E-3	-3.3638359148829802E-2	Orr_Coyne_1989_Drosophila	fertility_table2&3		Parents: ?? (mass culture)_SD estimated by Wan et al 2014
Drosophila_montana	Drosophila_virilis	Drosophila_montana_Drosophila_virilis	Drosophila_montana_Drosophila_virilis	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-1.0457575				0.99	NA	NA
Drosophila_persimilis	Drosophila_pseudoobscura	Drosophila_persimilis_Drosophila_pseudoobscura	Drosophila_persimilis_Drosophila_pseudoobscura	Overlap	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-2.2218487					NA	NA
Drosophila_quadraria	Drosophila_biauraria	Drosophila_quadraria_Drosophila_biauraria	Drosophila_quadraria_Drosophila_biauraria	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-2.3979400					NA	NA
Drosophila_quadraria	Drosophila_subauraria	Drosophila_quadraria_Drosophila_subauraria	Drosophila_quadraria_Drosophila_subauraria	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-1.4814861					NA	NA
Drosophila_triauraria	Drosophila_biauraria	Drosophila_triauraria_Drosophila_biauraria	Drosophila_triauraria_Drosophila_biauraria	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-1.4814861					NA	NA
Drosophila_triauraria	Drosophila_quadraria	Drosophila_triauraria_Drosophila_quadraria	Drosophila_triauraria_Drosophila_quadraria	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-3.0000000					NA	NA
Coturnix_coturnix_coturnix	Coturnix_japonica	Coturnix_coturnix_coturnix_Coturnix_japonica	Coturnix_coturnix_coturnix_Coturnix_japonica	Allopatry	original study	Aves	Ezaz Tariq et al. “Relationships between vertebrate ZW and XY sex chromosome systems.” Current Biology 16.17 (2006): R736-R743.	ZW	Female	-1.7447275	0.347634	0.38592000000000004	0.32175000000000004		0.10447989191213911	-7.7375355710390822E-2	Deregnaucourt_2002_Quail	table2&3
Drosophila_sechellia	Drosophila_simulans	Drosophila_sechellia_Drosophila_simulans	f^2	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-1.3872161	NA_possibly_high	NA_possibly_high	NA_possibly_high	NA_possibly_high	NA	NA
Drosophila_sechellia	Drosophila_simulans	Drosophila_sechellia_Drosophila_simulans	FC	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-1.3872161	NA_possibly_high	NA_possibly_high	NA_possibly_high	NA_possibly_high	NA	NA
Gryllus_armatus	Gryllus_rubens	Gryllus_armatus_Gryllus_rubens	Gryllus_armatus_Gryllus_rubens	Allopatry	original paper	Orthoptera	Castillo Elio Rodrigo Dardo Andrea Marti and Claudio Juan Bidau. “Sex and neo-sex chromosomes in Orthoptera: a review.” Journal of Orthoptera Research (2010): 213-231.	X0	Male						NA	NA				N of indv_N of songs. N of parental pairs: 12
Gryllus_campestris	Gryllus_rubens	Gryllus_campestris_Gryllus_rubens	Gryllus_campestris_Gryllus_rubens	Allopatry	original paper	Orthoptera	Castillo Elio Rodrigo Dardo Andrea Marti and Claudio Juan Bidau. “Sex and neo-sex chromosomes in Orthoptera: a review.” Journal of Orthoptera Research (2010): 213-231.	X0	Male	0.6626634					NA	NA				N of indv_N of songs. N of parental pairs: 12
Gryllus_rubens	Gryllus_texensis	Gryllus_rubens_Gryllus_texensis	Gryllus_rubens_Gryllus_texensis	Overlap	original study	Orthoptera	Castillo Elio Rodrigo Dardo Andrea Marti and Claudio Juan Bidau. “Sex and neo-sex chromosomes in Orthoptera: a review.” Journal of Orthoptera Research (2010): 213-231.	X0	Male	-2.0457575	76.5	69.099999999999994	52.4	43.8	0.13871328835293623	-0.13793485109369141	Cade_1989_Gryllus.mortality	table1		integerはtexensis
Drosophila_madeirensis	Drosophila_subobscura	Drosophila_madeirensis_Drosophila_subobscura	H27	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	0.5435714					NA	NA
Haplochromis_burtoni	Haplochromis_nubilus	Haplochromis_burtoni_Haplochromis_nubilus	Haplochromis_burtoni_Haplochromis_nubilus	Allopatry	original study	Cichliformes							0		NA	NA
Hyla_chrysoscelis	Hyla_femoralis	Hyla_chrysoscelis_Hyla_femoralis	Hyla_chrysoscelis_Hyla_femoralis	Overlap	original study	Anura	Miura I. “The late replication banding patterns of chromosomes are highly conserved in the genera Rana Hyla and Bufo (Amphibia: Anura).” Chromosoma 103.8 (1995): 567-574.	XY	Male	-0.0070049					NA	NA
Laupala_kohalensis	Laupala_paranigra	Laupala_kohalensis_Laupala_paranigra	KW L. paranigra	Allopatry	original study	Orthoptera	Castillo Elio Rodrigo Dardo Andrea Marti and Claudio Juan Bidau. “Sex and neo-sex chromosomes in Orthoptera: a review.” Journal of Orthoptera Research (2010): 213-231.	X0	Male						NA	NA
Drosophila_madeirensis	Drosophila_subobscura	Drosophila_madeirensis_Drosophila_subobscura	mad	Overlap	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	0.5435714					NA	NA
Drosophila_madeirensis	Drosophila_subobscura	Drosophila_madeirensis_Drosophila_subobscura	Mdh86	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	0.5435714					NA	NA
Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	musculus.1	Allopatry	original study	Rodentia	Ezaz Tariq et al. “Relationships between vertebrate ZW and XY sex chromosome systems.” Current Biology 16.17 (2006): R736-R743.	XY	Male	-1.5376020					NA	NA				N of indv_genetic distance_first 20 sequences were used_respectively
Mus_musculus_domesticus	Mus_musculus_musculus	Mus_musculus_domesticus_Mus_musculus_musculus	musculus.3	Allopatry	original study	Rodentia	Ezaz Tariq et al. “Relationships between vertebrate ZW and XY sex chromosome systems.” Current Biology 16.17 (2006): R736-R743.	XY	Male	-1.5376020					NA	NA				N of indv_genetic distance_first 20 sequences were used_respectively
Laupala_kohalensis	Laupala_paranigra	Laupala_kohalensis_Laupala_paranigra	OF L. paranigra	Allopatry	original study	Orthoptera	Castillo Elio Rodrigo Dardo Andrea Marti and Claudio Juan Bidau. “Sex and neo-sex chromosomes in Orthoptera: a review.” Journal of Orthoptera Research (2010): 213-231.	X0	Male						NA	NA
Drosophila_sechellia	Drosophila_simulans	Drosophila_sechellia_Drosophila_simulans	Oxnard	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-1.3872161	NA_possibly_high	NA_possibly_high	NA_possibly_high	NA_possibly_high	NA	NA
Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	parallelus_localityA	Overlap	original study	Orthoptera	Castillo Elio Rodrigo Dardo Andrea Marti and Claudio Juan Bidau. “Sex and neo-sex chromosomes in Orthoptera: a review.” Journal of Orthoptera Research (2010): 213-231.	X0	Male						NA	NA
Chorthippus_parallelus_erythropus	Chorthippus_parallelus_parallelus	Chorthippus_parallelus_erythropus_Chorthippus_parallelus_parallelus	parallelus_localityB	Overlap	original study	Orthoptera	Castillo Elio Rodrigo Dardo Andrea Marti and Claudio Juan Bidau. “Sex and neo-sex chromosomes in Orthoptera: a review.” Journal of Orthoptera Research (2010): 213-231.	X0	Male						NA	NA
Drosophila_mauritiana	Drosophila_sechellia	Drosophila_mauritiana_Drosophila_sechellia	pn_j_irX85	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-1.4814861			0		NA	NA
Pundamilia_nyererei	Pundamilia_pundamilia	Pundamilia_nyererei_Pundamilia_pundamilia	Pundamilia_nyererei_Pundamilia_pundamilia	Overlap	original study	Cichliformes					4.8600000000000003	4.84	4.51	4.57	2.616580464311944E-2	-4.445176257083381E-2	Van Der Sluijs_2008_Cichlid	text
Drosophila_sechellia	Drosophila_simulans	Drosophila_sechellia_Drosophila_simulans	RM	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-1.3872161	NA_possibly_high	NA_possibly_high	NA_possibly_high	NA_possibly_high	NA	NA
Drosophila_sechellia	Drosophila_simulans	Drosophila_sechellia_Drosophila_simulans	RMdet	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-1.3872161	NA_possibly_high	NA_possibly_high	NA_possibly_high	NA_possibly_high	NA	NA
Laupala_kohalensis	Laupala_paranigra	Laupala_kohalensis_Laupala_paranigra	SB L. paranigra	Allopatry	original study	Orthoptera	Castillo Elio Rodrigo Dardo Andrea Marti and Claudio Juan Bidau. “Sex and neo-sex chromosomes in Orthoptera: a review.” Journal of Orthoptera Research (2010): 213-231.	X0/XY	Male						NA	NA
Drosophila_auraria	Drosophila_biauraria	Drosophila_auraria_Drosophila_biauraria	sn.cn.ba2.auraria_B16.biauraria	Overlap	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-1.4814861					NA	NA				parental data provided in other paper (Tomaru.Oguma_1993.AnimBehav)_N: n of song_Parents: n of indv (mass culture)
Spodoptera_descoinsi	Spodoptera_latifascia	Spodoptera_descoinsi_Spodoptera_latifascia	Spodoptera_descoinsi_Spodoptera_latifascia	Overlap	original paper	Lepidoptera	Traut W. K. Sahara and F. Marec. “Sex chromosomes and sex determination in Lepidoptera.” Sexual Development 1.6 (2007): 332-346.	ZW/Z0	Female	-1.4685211					NA	NA
Teleogryllus_commodus	Teleogryllus_oceanicus	Teleogryllus_commodus_Teleogryllus_oceanicus	Teleogryllus_commodus_Teleogryllus_oceanicus	Overlap	The Australian crickets (Orthoptera: Gryllidae)	Orthoptera	Castillo Elio Rodrigo Dardo Andrea Marti and Claudio Juan Bidau. “Sex and neo-sex chromosomes in Orthoptera: a review.” Journal of Orthoptera Research (2010): 213-231.	X0	Male	0.1182647					NA	NA				N of indv_N of songs. N of parental pairs: 12
Drosophila_melanogaster	Drosophila_simulans	Drosophila_melanogaster_Drosophila_simulans	v	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-1.2924298	1	0.5	0.2	1	-0.69314718055994529	-1.6094379124341003	reviewed in Sawamura_
Drosophila_melanogaster	Drosophila_simulans	Drosophila_melanogaster_Drosophila_simulans	XsimYmel	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-1.2924298	1	0.5	0.2	1	-0.69314718055994529	-1.6094379124341003	reviewed in Sawamura_
Drosophila_sechellia	Drosophila_simulans	Drosophila_sechellia_Drosophila_simulans	XXY	Allopatry	original study	Diptera	Vicoso Beatriz and Doris Bachtrog. “Numerous transitions of sex chromosomes in Diptera.” PLoS biology 13.4 (2015): e1002078.	XY	Male	-1.3872161	NA_possibly_high	NA_possibly_high	NA_possibly_high	NA_possibly_high	NA	NA

#++++++++++++++++++++++++++++++++++++++++++++++
# Combine phenotypic data and moderators data
#++++++++++++++++++++++++++++++++++++++++++++++
original.dat <- left_join(pheno, meta)
# add unique ID for each effect size (row)
original.dat$ES.ID <- paste("ES",sprintf("%03d",c(1:dim(original.dat)[1])), sep="")

# #+++++++++++++++++++++++++++++++++
# print("Scaling")
# #+++++++++++++++++++++++++++++++++
# print("Phenotypic divergence ratio")
# attributes(pheno$Pheno.divergence)
# print("Minimum scaled value")
# min(pheno$Pheno.divergence)
# print("Maximum scaled value")
# max(pheno$Pheno.divergence)

Reciprocal: Viability of reciprocal hybrids, with two levels - Viable or Inviable

Genet.divergence: Natural logarithm of genetic distance at mtDNA COI region (>450bp).

Distribution: Distribution overlap - Allopatry or Overlap

Hetero.sex: Heterogametic sex - Female (ZW and Z0) or Male (XY and X0)

Species composition

#+++++++++++++++++++++#
# Phylogeny
#+++++++++++++++++++++#

# species pairs data 
taxa <- meta %>%
  select(-Cross.ID) %>%
  distinct_all(.keep_all = TRUE) %>%
  mutate_at("taxa", as.factor)

# matching names from open tree taxonomy
order <- tnrs_match_names(
  names = levels(taxa$taxa) %>%
    str_replace_all("_", " "), 
  context_name = "Animals"
  )

# which names return more than 1 match?
multimatch <- order$ott_id[order$number_matches != 1]
# inspect(order, ott_id = multimatch[1]) # Anura, confirmed adopted ott_id is correct
# inspect(order, ott_id = multimatch[2]) # Neuroptera, confirmed adopted ott_id is correct

# Create taxonomic tree
tree <- tol_induced_subtree(ott_ids = order$ott_id)

# Remove ott ids from tip label
tree$tip.label %<>%
  strip_ott_ids(remove_underscores=TRUE)
tree$tip.label[8] <- "Anura"

# # N of species pairs at each order
# factor(meta.sum$taxa, levels = as.vector(tree$tip.label))

plot.phylo <- ggtree(tree) + 
  geom_tiplab(
    geom = "image",
    image = paste("../data/pic", tree$tip.label, "png", sep = "."), 
    size = 0.1, offset = 3, hjust = 1
    ) +
  geom_tiplab(
    geom = "text",  
    label = tree$tip.label, 
    align = 1, size = 3
    ) +
  xlim(NA, 7)

# images from phylopic #    
# Lepidoptera (genus Catocala) "c224abfd-ee39-4923-98e5-c2606dcc56cb", 
# Diptera (Drosophila) "0cd6cc9f-683c-470e-a4a6-3b68beb826fa"
# Orthoptera (cricket) "b80d830b-155a-4ca5-9119-9a9fde019cc6"
# Rodentia (mouse), drawn by David Liao "0f6af3d8-49d2-4d75-8edf-08598387afde"
# Aves (Quail) "42f85a2b-7517-439a-8dc3-b745a35c035d"
# Anura (genus Hyla) "A22D353D-B045-4044-9432-8F340B9A3104", 
# Cichliformes (Nile tilapia), drawn by Milton Tan "84c7e672-2593-44a6-a807-cffbd3156cc5" 


#+++++++++++++++++++++#
# Summary 
#+++++++++++++++++++++#

summary <- original.dat %>%
  group_by(taxa) %>%
  summarise(
    'Species pair' = length(unique(species.pair)),
    'Obser- vation' = length(unique(ES.ID)),
    Study = length(unique(Study.ID))
    ) %>%
  # Order taxon so that match with phylogenetic tree topology
  within(taxa <- ordered(taxa, levels = c("Neuroptera", "Coleoptera", "Diptera", "Lepidoptera", "Orthoptera", "Aves", "Rodentia", "Anura", "Cichliformes")))


## Table ###
summary %>%
  summarise_if(is.numeric, sum) %>%
    mutate(taxa = "Total") %>%
  bind_rows(summary, .) %>%
  kable("html", digits = 3) %>% 
  kable_styling("striped", position = "left")

taxa	Species pair	Obser- vation	Study
Anura	1	5	1
Aves	1	1	1
Cichliformes	2	6	3
Coleoptera	1	2	1
Diptera	23	65	9
Lepidoptera	2	13	2
Neuroptera	1	6	1
Orthoptera	7	63	6
Rodentia	1	240	1
Total	39	401	25

## Plot ###
plot.sum <- ggplot(
  # Make tidy summary data
  summary %>%
  gather(key = metrics, value = N, -c(taxa)) %>%
  # Order metrics
  within(
    metrics <- ordered(
      metrics,
      levels = c("Study", "Species pair", "Obser- vation")
      )
    ), 
  aes(x = "", y = N, fill = taxa)) +
  geom_bar(width = 1, stat = "identity") +
  scale_fill_brewer(palette = "Paired") +
  facet_wrap(
    scale = "free", "metrics",
    # strip text into two lines
    labeller = label_wrap_gen(width = 8)
    ) +
  xlab("") +
  stackedbartheme +
  theme(
    strip.text = element_text(size = 15),
    axis.text.y = element_text(size = 14)
    )

#+++++++++++++++++#
# Combine figures
#+++++++++++++++++#
# plot.all <- plot.phylo + plot.sum
plot.sum

# save(
#   plot = plot.all, 
#   file = "../Analysis/data.svg", height = 5, width = 8.5
#   )

Figure 1a

Taxonomic distribution of species-level moderator status

Summarised distributions of categorical species-level moderators in each taxon (order).

metavars <- c("Reciprocal", "Distribution", "Hetero.sex")

meta.unique <- original.dat %>%
  distinct_at("species.pair", .keep_all = TRUE) %>%
  mutate_at(metavars, as.factor)

meta.all.sp <- meta.unique %>% 
  group_by(taxa) %>%
  summarise(Total = length(species.pair)) 

for (i in metavars) {
  
  bind_rows(
    # At each taxon
    meta.unique %>% 
      group_by_("taxa", i) %>%
      summarise(species = length(species.pair))
    ,
    # All taxon
    meta.unique %>% 
      group_by_(i) %>%
      summarise(species = length(unique(species.pair))) %>% 
      mutate(taxa = "Total")
    ) %>%
    ungroup %>%
    spread(key = i, value = "species") %>%
    mutate_all(~replace(., is.na(.), 0)) %>% # replace NA by 0
    as.data.frame %>%
    # All species pair at each taxon: summing columns
    mutate(Total = rowSums(.[2:(1+nlevels(meta.unique[, i]))])) %>%
    # Calc % of level 2 (i.e. male hetero, ...)
    mutate('%' = round(.[3]/Total*100, 1)) %>%
    rename(!!paste0(levels(meta.unique[, i])[2], "%") := "%") %>%
    # Table output
    kable("html", digits = 3, caption = i) %>% 
    kable_styling("striped", position = "left") %>%
    print()

}  

Reciprocal

taxa	Inviable	Viable	Total	Viable%
Anura	0	1	1	100.0
Aves	0	1	1	100.0
Cichliformes	1	1	2	50.0
Coleoptera	0	1	1	100.0
Diptera	11	12	23	52.2
Lepidoptera	1	1	2	50.0
Neuroptera	0	1	1	100.0
Orthoptera	2	5	7	71.4
Rodentia	1	0	1	0.0
Total	16	23	39	59.0

Distribution

taxa	Allopatry	Overlap	Total	Overlap%
Anura	0	1	1	100.0
Aves	1	0	1	0.0
Cichliformes	1	1	2	50.0
Coleoptera	0	1	1	100.0
Diptera	19	4	23	17.4
Lepidoptera	0	2	2	100.0
Neuroptera	0	1	1	100.0
Orthoptera	3	4	7	57.1
Rodentia	1	0	1	0.0
Total	25	14	39	35.9

Hetero.sex

taxa	Female	Male	<NA>	Total	Male%
Anura	0	1	0	1	100.0
Aves	1	0	0	1	0.0
Cichliformes	0	0	2	0
Coleoptera	0	1	0	1	100.0
Diptera	0	23	0	23	100.0
Lepidoptera	2	0	0	2	0.0
Neuroptera	0	1	0	1	100.0
Orthoptera	0	7	0	7	100.0
Rodentia	0	1	0	1	100.0
Total	3	34	2	37	91.9

Data used in meta-regression (all metadata available)

full_join(
  # All species pair
  original.dat %>%
    select(c("taxa", "species.pair")) %>%
    distinct_all() %>%
    group_by(taxa) %>%
    summarise('All species' = length(species.pair)),
  # Species pair used in meta-regression
  original.dat %>%
    filter(!is.na(sex.determination), !is.na(Genet.divergence)) %>%
    select(c("taxa", "species.pair")) %>%
    distinct_all() %>%
    group_by(taxa) %>%
    summarise('Metadata available' = length(species.pair))
  ) %>%
  mutate_all(~replace(., is.na(.), 0)) %>% # replace NA by 0
  kable("html", caption = "Numbers of all species pairs, and species pairs used in meta-regression
") %>% 
  kable_styling("striped", position = "left")

Numbers of all species pairs, and species pairs used in meta-regression

taxa	All species	Metadata available
Anura	1	1
Aves	1	1
Cichliformes	2	0
Coleoptera	1	1
Diptera	23	23
Lepidoptera	2	2
Neuroptera	1	1
Orthoptera	7	4
Rodentia	1	1

Two fish and three orthoptera species-pairs were not used in meta-regression and Bayesian phylogenetic mixed models because of the lack of data for genetic distance or sex-determination system (fish, Haplochromis burtoni × H. nubilus and Pundamilia nyererei × P. pundamilia; orthoptera, Chorthippus parallelus erythropus × C. parallelus parallelus, Gryllus armatus × G. rubens and Laupala kohalensis × L. paranigra). We did not assign value of the heterogametic sex in cichlid fishes that undergo frequent evolutionary changes in sex-determination system Yoshida et al. 2011.

Data processing

Allign parental species according to trait values

Currently, dataset is composed of

‘species 1’ and ‘species 2,’ ordered alphabetically, and ‘hybrid12’ (sp1 mother & sp2 father) and ‘hybrid 21’ (sp2 mother & sp1 father)

To investigate paternal effect in hybrids using difference in mean trait value between reciprocal cross, it’s better to allign species with larger trait value (spLL_M) and smaller value (spSS_M) in each traits. Name of hybrids will be changed according to change in names of parental species (hybridLS_M & hybridSL_M).

#+++++++++++++++++++++++++++++++++++++++++++++++++#
# sp1 >= sp2 in phenotypic mean 
#+++++++++++++++++++++++++++++++++++++++++++++++++#
# extract
sp1sp2.mean <- original.dat %>%
  filter(Mn.sp1 >= Mn.sp2) # filtering according to mean trait value of parentals
# change column name from sp1 or sp2 -> spL or spS
names(sp1sp2.mean) <- gsub("1", "L", names(sp1sp2.mean))
names(sp1sp2.mean) <- gsub("2", "S", names(sp1sp2.mean))

#+++++++++++++++++++++++++++++++++++++++++++++++++#
# sp1 < sp2 in phenotypic mean 
#+++++++++++++++++++++++++++++++++++++++++++++++++#
# extract
sp2sp1.mean <- original.dat %>%
  filter(Mn.sp1 < Mn.sp2)
# change column name from sp1 or sp2 -> spS or spL
names(sp2sp1.mean) <- gsub("1", "S", names(sp2sp1.mean))
names(sp2sp1.mean) <- gsub("2", "L", names(sp2sp1.mean))

#+++++++++++++++++++++++++++++++++++++++++++++++++#
# combine dataset & sort by ES.ID 
#+++++++++++++++++++++++++++++++++++++++++++++++++#
bind_rows(sp1sp2.mean, sp2sp1.mean) %>%
  arrange(ES.ID) %>%
  write.csv("../data/dat.mean.csv", quote=F,row.names = F)

Data set created here, dat.mean.csv, is used in calculating effect sizes for analyses investigating phenotypic mean and novelty in phenotypic means.

Allign parental species according to phenotypic variation (CV)

To investigate paternal effect in hybrids using difference in coefficient of variation (CV) between reciprocal cross, it’s better to allign species with larger CV (spLL_V) and smaller CV (spSS_V) in each traits. Name of hybrids will be changed according to change in names of parental species (hybridLS_V & hybridSL_V).

#+++++++++++++++++++++++++++++++++++++++++++++++++#
# sp1 >= sp2 in phenotypic variation (CV)
#+++++++++++++++++++++++++++++++++++++++++++++++++#
# extract
sp1sp2.var <- original.dat %>%
  filter(SD.sp1/Mn.sp1 >= SD.sp2/Mn.sp2) # filtering according to CV of parentals
# change column name from sp1 or sp2 -> spL or spS
names(sp1sp2.var) <- gsub("1", "L", names(sp1sp2.var))
names(sp1sp2.var) <- gsub("2", "S", names(sp1sp2.var))

#+++++++++++++++++++++++++++++++++++++++++++++++++#
# sp1 < sp2 in phenotypic variation (CV)
#+++++++++++++++++++++++++++++++++++++++++++++++++#
# extract
sp2sp1.var <- original.dat %>%
  filter(SD.sp1/Mn.sp1 < SD.sp2/Mn.sp2)
# change column name from sp1 or sp2 -> spS or spL
names(sp2sp1.var) <- gsub("1", "S", names(sp2sp1.var))
names(sp2sp1.var) <- gsub("2", "L", names(sp2sp1.var))

#+++++++++++++++++++++++++++++++++++++++++++++++++#
# combine dataset & sort by ES.ID 
#+++++++++++++++++++++++++++++++++++++++++++++++++#
bind_rows(sp1sp2.var, sp2sp1.var) %>%
  arrange(ES.ID) %>%
  write.csv("../data/dat.CV.csv", quote=F,row.names = F)

Data set created here, dat.CV.csv, is used in calculating effect sizes for analyses investigating phenotypic variation and novelty in phenotypic variabilities.

Simultaneously judge novel phenotype and variability expression

We categorised phenotypic novelty (novel or non-novel) and relative phenotypic variability (CV) compared to parents (smaller, intermediate, larger) at each trait.

bind_rows(
  # Assess novel phenotype & variability expression in hyb12
  original.dat %>%
    drop_na(Mn.hyb12, SD.hyb12) %>%
    mutate(
      cross = "1x2",
      # Relative trait size
      relative.mean = ifelse(
        # Exceeding upper range
        Mn.hyb12 > Mn.sp1 & Mn.hyb12 > Mn.sp2,
        "Larger",
        ifelse(
          # Exceeding lower range
          Mn.hyb12 < Mn.sp1 & Mn.hyb12 < Mn.sp2,
          "Smaller",
          "Intermediate"
        )
      ),
      # Relative variability size
      relative.var = ifelse(
        # Exceeding upper range: 1
        SD.hyb12/Mn.hyb12 > SD.sp1/Mn.sp1 & 
          SD.hyb12/Mn.hyb12 > SD.sp2/Mn.sp2,
        "Larger",
        ifelse(
          # Exceeding lower range: "Smaller"
          SD.hyb12/Mn.hyb12 < SD.sp1/Mn.sp1 & 
            SD.hyb12/Mn.hyb12 < SD.sp2/Mn.sp2,
          "Smaller",
          "Intermediate"
        )
      )    
    ),
  # Assess novel phenotype & variability expression in hyb12
  original.dat %>%
    drop_na(Mn.hyb21, SD.hyb21) %>%
    mutate(
      cross = "2x1",
      # Relative trait size
      relative.mean = ifelse(
        # Exceeding upper range: 1
        Mn.hyb21 > Mn.sp1 & Mn.hyb21 > Mn.sp2,
        "Larger",
        ifelse(
          # Exceeding lower range: "Smaller"
          Mn.hyb21 < Mn.sp1 & Mn.hyb21 < Mn.sp2,
          "Smaller",
          "Intermediate"
        ) 
      ) ,
      # Relative variability size
      relative.var = ifelse(
        # Exceeding upper range: 1
        SD.hyb21/Mn.hyb21 > SD.sp1/Mn.sp1 & 
          SD.hyb21/Mn.hyb21 > SD.sp2/Mn.sp2,
        "Larger",
        ifelse(
          # Exceeding lower range: "Smaller"
          SD.hyb21/Mn.hyb21 < SD.sp1/Mn.sp1 & 
            SD.hyb21/Mn.hyb21 < SD.sp2/Mn.sp2,
          "Smaller",
          "Intermediate"
        )
      )    
    ) 
  ) %>%
  mutate(mean.novelty = ifelse(relative.mean == "Intermediate", "Nonnovel", "Novel")) %>%
  write.csv("../data/dat.novelty.csv", quote=F,row.names = F)

Data set created here, dat.novelty.csv, is used in investigating phenotypic variability of novel phenotype.

Meta-analysis for phenotypic mean

We created data subset according to taxon (all taxon or insect only) and novelty in phenotypic means (all traits or non-novel traits - trait size of hybrids does not exceed size range of parents). We conduct identical analyses for full data and data subsets, and checked robustness of our results.

Calculating effect sizes

To quantify phenotypic difference from spSS_M to the other crosses, we calculated log response ratio (lnRR) from phenotypic mean of spSS_M to those of the other crosses (hybLS_M, hybSL_M and spLL_M).
Effecct size was calculated by using escalc function in metafor package

# load phenotypic data
dat <- read.csv("../data/dat.mean.csv", head = TRUE)

# Order by taxon
dat$taxa <- ordered(
  dat$taxa,
  levels = c("Neuroptera", "Coleoptera", "Diptera", "Lepidoptera", "Orthoptera", "Aves", "Rodentia", "Anura", "Cichliformes")
  )

## Difference between hybrids and smaller species (spS) ###
mean.dif <- foreach(
  cr = c("hybLS", "hybSL", "spL"), 
  .combine = `rbind`
  ) %do% {
    ## lnRR ###
    escalc(
      measure = "ROM",
      # N of hybrids
      n1i = dat[, paste("N", cr, sep = ".")],
      # N of parentals
      n2i = dat[, "N.spS"],
      # Mean of hybrids
      m1i = dat[, paste("Mn", cr, sep = ".")],
      # Mean of parentals
      m2i = dat[, "Mn.spS"],
      # SD of hybrids
      sd1i = dat[, paste("SD", cr, sep = ".")],
      # SD of parentals
      sd2i = dat[, "SD.spS"],
      data = dat
      ) %>%
      rename(lnRR.es = yi, lnRR.sv = vi) %>%
    # indicate type of hybrid
    mutate(cross = cr)
    } %>% 
  drop_na(lnRR.sv) %>%
  arrange(ES.ID) %>%
  left_join(., dat) 
  
# Dataset able to compare reciprocal crosses
write.csv(
  mean.dif %>%
    filter(Reciprocal == "Viable"), 
  "../data/mean.ES.general.csv", row.names = F
  )

Funnel plots

res <- rma(
  yi = lnRR.es, vi = lnRR.sv, 
  data = mean.dif, method="FE"
  )

## set up 2x2 array for plotting
par(mfrow=c(2,2))
 
## draw funnel plots
funnel(res, main="Standard Error", xlim = c(-2, 4))
funnel(res, yaxis="vi", main="Sampling Variance", xlim = c(-2, 4))
funnel(res, yaxis="seinv", main="Inverse Standard Error", xlim = c(-2, 4))
funnel(res, yaxis="vinv", main="Inverse Sampling Variance", xlim = c(-2, 4))

Check data

Checked effect sizes with extremely high/low standard error (SE > 1.2 | 1/SE > 140) and confirmed that those data were correctly imported.

outliers <- mean.dif %>%
  # vi (sampling variance) 
  mutate(SE = sqrt(lnRR.sv)) %>%
  arrange(desc(SE)) %>%
  filter(SE > 1.2 | 1/SE > 140) %>%
  select(ES.ID, Study.name, SE, cross, Reciprocal, trait, Cross.ID) %>%
  mutate_if(is.numeric, round, 3)

outliers %>%
  kable("html", digits = 3, caption = "Effect sizes with extremely high/low standard error (SE > 1.2 | 1/SE > 140)") %>% 
  kable_styling("striped", position = "left") %>%
  scroll_box(width = "100%", height = "500px")

Effect sizes with extremely high/low standard error (SE > 1.2 | 1/SE > 140)

ES.ID	Study.name	SE	cross	Reciprocal	trait	Cross.ID
ES320	Musolf et al_2015	21.535	spL	Inviable	Frequ Modulated Downsweep_FAC1_2	musculus.3
ES320	Musolf et al_2015	21.470	hybSL	Inviable	Frequ Modulated Downsweep_FAC1_2	musculus.3
ES253	Musolf et al_2015	14.218	hybLS	Inviable	Complex 2_FAC2_1	musculus.3
ES253	Musolf et al_2015	9.883	spL	Inviable	Complex 2_FAC2_1	musculus.3
ES147	Musolf et al_2015	6.075	spL	Inviable	U Shaped Inverted_FAC1_2	musculus.1
ES123	Musolf et al_2015	2.073	hybLS	Inviable	Complex 2_FAC2_1	musculus.1
ES367	Suvanto et al_1994	1.761	hybLS	Viable	inhbt_IPI	Drosophila_borealis_Drosophila_virilis
ES371	Suvanto et al_1994	1.538	spL	Inviable	inhbt_PL	Drosophila_montana_Drosophila_virilis
ES367	Suvanto et al_1994	1.375	hybSL	Viable	inhbt_IPI	Drosophila_borealis_Drosophila_virilis
ES125	Musolf et al_2015	1.276	spL	Inviable	Complex 2_FAC1_4	musculus.1
ES123	Musolf et al_2015	1.231	spL	Inviable	Complex 2_FAC2_1	musculus.1
ES388	Tomaru&Oguma_1994	0.007	hybSL	Inviable	IPI	Drosophila_triauraria_Drosophila_biauraria
ES389	Tomaru&Oguma_1994	0.006	spL	Viable	IPI	Drosophila_quadraria_Drosophila_biauraria
ES055	Coyne.et.al_1991	0.006	hybSL	Viable	Tibia length	pn_j_irX85
ES036	Coyne_1983	0.006	spL	Inviable	WL	BGxMutant
ES039	Coyne_1983	0.006	hybLS	Viable	WL	BGxSPD(wildtype)
ES036	Coyne_1983	0.006	hybLS	Inviable	WL	BGxMutant
ES390	Tomaru&Oguma_1994	0.006	spL	Inviable	IPI	Drosophila_quadraria_Drosophila_subauraria
ES039	Coyne_1983	0.006	hybSL	Viable	WL	BGxSPD(wildtype)
ES039	Coyne_1983	0.006	spL	Viable	WL	BGxSPD(wildtype)
ES388	Tomaru&Oguma_1994	0.006	spL	Inviable	IPI	Drosophila_triauraria_Drosophila_biauraria
ES387	Tomaru&Oguma_1994	0.005	spL	Viable	IPI	Drosophila_triauraria_Drosophila_quadraria
ES391	Tomaru&Oguma_1994	0.005	spL	Viable	IPI	Drosophila_biauraria_Drosophila_subauraria
ES390	Tomaru&Oguma_1994	0.005	hybLS	Inviable	IPI	Drosophila_quadraria_Drosophila_subauraria

Because those outliers did not affect meta-analytic model convergence, we did not exclude those data.

Judging novelty in phenotypic means

# load spLS files
dat <- read.csv("../data/dat.mean.csv", head = TRUE)

#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#
# Difference between larger species (spL) and hybrids 
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#

for (i in c("spL")) { # identify type of hybrids
  for (cr in c("hybLS", "hybSL")) { # identify type of parentals
    assign(
      paste(cr, i, sep = "_"),
        ## lnRR ###
        escalc(
          measure = "ROM",
          # N of hybrids
          n1i = dat[, paste("N", i, sep = ".")],
          # N of parentals
          n2i = dat[, paste("N", cr, sep = ".")],
          # Mean of hybrids
          m1i = dat[, paste("Mn", i, sep = ".")],
          # Mean of parentals
          m2i = dat[, paste("Mn", cr, sep = ".")],
          # SD of hybrids
          sd1i = dat[, paste("SD", i, sep = ".")],
          # SD of parentals
          sd2i = dat[, paste("SD", cr, sep = ".")],
          data = dat
          ) %>%
        rename(lnRR.es = yi, lnRR.sv = vi) %>%
        select(ES.ID, contains("lnRR")) %>%
        mutate(
          # direction of transgressve segregation - greater than parent
          direction = "+",
          # SE for logistic distribution, for the weighted binomial regression
          # SE = pi/sqrt(3*(n_e + n_c))
          SE = pi/sqrt(3*(
            dat[, paste("N", i, sep = ".")] + dat[, paste("N", cr, sep = ".")]
          ))
        )
    )
  }
}

#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#
# Difference between hybrids and smaller species (spS)
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#

for (i in c("hybLS", "hybSL")) { # identify type of hybrids
  for (cr in c("spS")) { # identify type of parentals
    assign(
      paste(i, cr, sep = "_"),
        ## lnRR ###
        escalc(
          measure = "ROM",
          # N of hybrids
          n1i = dat[, paste("N", i, sep = ".")],
          # N of parentals
          n2i = dat[, paste("N", cr, sep = ".")],
          # Mean of hybrids
          m1i = dat[, paste("Mn", i, sep = ".")],
          # Mean of parentals
          m2i = dat[, paste("Mn", cr, sep = ".")],
          # SD of hybrids
          sd1i = dat[, paste("SD", i, sep = ".")],
          # SD of parentals
          sd2i = dat[, paste("SD", cr, sep = ".")],
          data = dat
          ) %>%
          rename(lnRR.es = yi, lnRR.sv = vi) %>%
        select(ES.ID, contains("lnRR")) %>%
        mutate(
          # direction of transgressve segregation - smaller than parent
          direction = "-",
          # SE for logistic distribution, for the weighted binomial regression
          # SE = pi/sqrt(3*(n_e + n_c))
          SE = pi/sqrt(3*(
            dat[, paste("N", i, sep = ".")] + dat[, paste("N", cr, sep = ".")]
          ))
        )
    )
  }
}

#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#
# Data output
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#

bind_rows(
  ## Difference with mother species ###
  bind_rows(hybLS_spL, hybSL_spS) %>%
    mutate(parental = "Mother"),
  ## Difference with father species ###
  bind_rows(hybLS_spS, hybSL_spL) %>%
    mutate(parental = "Father")
  ) %>%
  drop_na(lnRR.sv) %>%
  left_join(., dat) %>%
  # Assign novel phenotype to 1, non-novel phenotype to 0 for each effect size
  mutate(Novelty = ifelse(lnRR.es < 0, 1, 0)) %>%
  ## Join with metadata ###
  left_join(
    ., read.xlsx("../data/original.data.xlsx", sheet = "Species.level.moderators")
    ) %>%
  mutate_at(vars(contains("divergence")), as.numeric) %>%
  mutate_at(vars(contains("divergence")), scale) %>%
  as.data.frame %>%
  select(-trait) %>%
  mutate_at("taxa", as.factor) %>%
  within(levels(Novelty) <- c("No", "Novel phenotypic means")) %>%
  within(
    taxa <- ordered(
      taxa,
      levels = c("Neuroptera", "Coleoptera", "Diptera", "Lepidoptera", "Orthoptera", "Aves", "Rodentia", "Anura", "Cichliformes")
      )
    ) %>%
  # Remove rows without any metadata
  drop_na(Hetero.sex, Pheno.divergence, trait.type, Genet.divergence) %>%
  # Insect or no
  mutate(
    insect = ifelse(taxa %in% c("Aves", "Rodentia", "Anura", "Cichliformes"), "no", "insect"),
  ) %>%
  write.csv("../data/mean.ES.Novelty.csv", row.names = F)

Reload data & Prepare phylogenetic tree

# Load effect sizes
mean.dif <- read.csv("../data/mean.ES.general.csv", head = TRUE) %>%
  # indicate dataset including novel phenotype
  mutate(data.type = "Alltraits")

# All trait observation irrespective of phenotypic novelty
All <- read.csv("../data/mean.ES.Novelty.csv", head = TRUE) %>%
  filter(lnRR.es < 0) %>%
  distinct(ES.ID)

# Trait observation of non-novel phenotypes
Nonovel <- read.csv("../data/mean.ES.general.csv", head = TRUE) %>%
  filter(!(ES.ID %in% All$ES.ID)) %>%
  # indicate dataset without novel phenotype
  mutate(data.type = "Nonnoveltraits")

# combine dataset with/without novel phenotype
Novel.Nonnovel <- bind_rows(mean.dif, Nonovel) %>%
  mutate(metaunit =  str_c(data.type, cross, sep = "_"))%>%
  # Use observations with both reciprocal crosses
  drop_na(contains("Mn"), contains("SD")) %>%
  # Insect or no
  mutate(
    insect = ifelse(taxa %in% c("Cichliformes", "Anura", "Aves", "Rodentia"), "no", "insect"),
  )


#+++++++++++++++++++++++++++++++++++++++++++++++#
# Phylogeny
#+++++++++++++++++++++++++++++++++++++++++++++++#

# # matching names from open tree taxonomy
# taxa <- tnrs_match_names(
#   names = levels(mean.dif$spL.name) %>%
#     str_replace_all("_", " "), 
#   context_name = "Animals"
#   )
# 
# # which names return more than 1 match?
# inspect(taxa, ott_id = taxa$ott_id[taxa$number_matches != 1])
# 
# # fixing names with more than 1 match
# taxa[taxa$number_matches != 1, ] <- 
#   inspect(taxa, ott_id = taxa$ott_id[taxa$number_matches != 1])[1, ]
# 
# tree <- tol_induced_subtree(ott_ids = taxa$ott_id)
# tree$tip.label %<>%
#   strip_ott_ids(remove_underscores=TRUE)
# 
# print("Original tree from OTL")
# plot(tree, no.margin=TRUE)
# 
# # randomly solve non-binary phylogeny
# set.seed(6)
# bin.tree <- multi2di(tree, random = T) 
# print("Randomly solved phylogeny")
# plot(bin.tree)
# 
# # correlation matrix to fit to the model
# bin.tree$tip.label <- bin.tree$tip.label %>%
#   as.factor() 
# 
# levels(bin.tree$tip.label) <- levels(mean.dif$spL.name) # making sure names match
# bin.tree$tip.label <- as.character(bin.tree$tip.label) # converting names back to character
# 
# write.tree(bin.tree, file= "../data/phylo.mean.tre")

# compute branch lengths of tree
phylo_branch <- read.tree(file = "../data/phylo.mean.tre") %>%
  compute.brlen(bin.tree, method = "Grafen", power = 1)

# saving phylogeneic matrix
phylo_cor <- vcv(phylo_branch, cor = T)

# Note one of the tips is called "Lates calcarifer (estimated)"
phylo_branch$node.label <- NULL

# Plot tree
plot.phylo(phylo_branch, cex = 0.7)

Figure S1. Phylogenetic tree used in meta-analysis for phenotypic mean, which was based on tree of the parental species with larger phenotypic mean

Phylogenetic tree created based on taxonomic tree on Open Tree of Life. Non-binary tree was randomly solved. We used packages rotl, ape and phytools on R.

Dominance in phenotypic means

Compare midparent and hybrids by comparing difference from spSS_M phenotypic mean to midparent-value \(\frac{spLL_M+spSS_M}{2}\) and to hybrids’ phenotypic mean.

• lnRR of midparent to spSS_M: \(\ln\frac{spLL_M+spSS_M}{2spSS_M}\)
• lnRR of hybrids to spSS_M: \(\ln\frac{hybrid}{spSS_M}\)

As the variance of \(\ln\frac{spLL_M+spSS_M}{2spSS_M}\), we use the variance of \(\ln\frac{spLL_M}{spSS_M}\) for simplicity

We statistically compared those effect sizes through the formal meta-regression by using rma.mv function of R package metafor

All meta-analytic models included following Random effects estimates:

• Study.ID: Primary studies. Denoted as Study

• Cross.ID: Parental strain used in the crossing. Discriminate intraspecific populations. Denoted as crossed strain

• spL.name: Phylogeny of parental species (spLL_M). Denoted as species with phylogeny

#++++++++++++++++++++++++++++++++++++++++++++++++++++#
# Calculate midparent 
#++++++++++++++++++++++++++++++++++++++++++++++++++++#
midparent <- Novel.Nonnovel %>%
  # Divide mean by 2 in spL whereas hybrids are constant
  mutate(
    lnRR.midparent.es = 
      ifelse(
        cross == "spL",
        log(((Mn.spL + Mn.spS)/2)/Mn.spS), lnRR.es
        )
    # Divide variance by 4 in spL whereas hybrids are constant
    ) %>%
  mutate_at("cross", as.factor) %>%
  within(levels(cross) <- c("hybLS", "hybSL", "midparent"))


#+++++++++++++++++++++++++++++++++++++++++++++++++++#
# Compare midparent and hybrids by meta-regresson
#+++++++++++++++++++++++++++++++++++++++++++++++++++#
for (tr in c("Alltraits", "Nonnoveltraits")) {
  for (taxon in c("alltaxon", "insect")) {
    
    if (taxon == "alltaxon") {
      dat <- midparent %>% 
        filter(data.type == tr)
    } else {
      dat <- midparent %>% 
        filter(data.type == tr, insect == taxon)
    }
  
    # ## phylogenetic random regresson comparing with midparent ###
    # midparent.compare <- rma.mv(
    #   yi = lnRR.midparent.es,
    #   V = lnRR.sv,
    #   data = dat,
    #   method = "REML",
    #   random = list(~1 | spL.name, ~1 | Study.ID, ~1 | Cross.ID, ~1 | ES.ID),
    #   R = list(spL.name = phylo_cor),
    #   mods = ~ relevel(cross, ref = "midparent")
    #   )
    # ## Save model ###
    # saveRDS(
    #   midparent.compare,
    #   file = paste("../Analysis/Mean.midparent.compare", tr, taxon, "obj", sep = ".")
    #   )

    ## Load model ###
    midparent.compare <- readRDS(
      paste("../Analysis/Mean.midparent.compare", tr, taxon, "obj", sep = ".")
      )
    assign(
      paste("Dominance", tr, taxon, sep = "."),
      midparent.compare %>% 
        get_reg() %>%
        # Show dataset name
        within(Dataset <- c(paste(tr, taxon), rep("", length(.$Estimate)-1))) %>%
        # Rename fixed effects
        within('Fixed effects' <- c("", "midparent (intrcpt)", "hybLS", "hybSL")) %>%
        # Difference of each cross from midparent in %
        mutate('Comparison with midparent' = c(
          rep("", 2),
          paste(
            round(
            100*(exp(midparent.compare$beta[1]+midparent.compare$beta[2])            -exp(midparent.compare$beta[1])), # hybrid LS
            2),
            "% larger"
            ),
          paste(
            round(
            100*(exp(midparent.compare$beta[1]+midparent.compare$beta[3])-exp(midparent.compare$beta[1])), # hybrid SL
            2),
            "% larger"
            )
          ))
      )
    
    # ## Meta-analysis for midparent to plot band ###
    # midparent.random <- rma.mv(
    #   yi = lnRR.midparent.es,
    #   V = lnRR.sv,
    #   data = dat %>% filter(cross == "midparent"),
    #   method = "REML",
    #   random = list(~1 | spL.name, ~1 | Study.ID, ~1 | Cross.ID, ~1 | ES.ID),
    #   R = list(spL.name = phylo_cor)
    #   )
    # ## Save model ###
    # saveRDS(
    #   midparent.random,
    #   file = paste("../Analysis/Mean.midparent", tr, taxon, "obj", sep = ".")
    #   )

  }
}

bind_rows(
  Dominance.Alltraits.alltaxon, Dominance.Nonnoveltraits.alltaxon,
  Dominance.Alltraits.insect, Dominance.Nonnoveltraits.insect
  ) %>%
  kable("html", digits = 3, caption = "Table S3. Results of meta-analysis investigating dominance in phenotypic means using full dataset or data subsets") %>% 
  kable_styling("striped", position = "left") %>%
  scroll_box(width = "100%", height = "500px")

Table S3. Results of meta-analysis investigating dominance in phenotypic means using full dataset or data subsets

Dataset	Fixed effects	Estimate	LowerCI	UpperCI	P	I2[total]	%I2[species with phylogeny]	%I2[study]	%I2[crossed strain]	%I2[residual]	significance	Comparison with midparent
Alltraits alltaxon						99.7%	38.8%	1.1%	0%	59.8%
	midparent (intrcpt)	0.578	0.266	0.890	0
	hybLS	-0.073	-0.079	-0.067	0							-12.53 % larger
	hybSL	-0.100	-0.106	-0.093	0							-16.9 % larger
Nonnoveltraits alltaxon						99.7%	34.9%	1.3%	0%	63.5%
	midparent (intrcpt)	0.677	0.370	0.984	0
	hybLS	-0.142	-0.149	-0.134	0							-26 % larger
	hybSL	-0.189	-0.196	-0.181	0							-33.84 % larger
Alltraits insect						99.6%	0%	10%	0%	89.6%
	midparent (intrcpt)	0.396	0.306	0.486	0
	hybLS	-0.073	-0.079	-0.066	0							-10.42 % larger
	hybSL	-0.100	-0.106	-0.094	0							-14.15 % larger
Nonnoveltraits insect						99.5%	0%	9.1%	0%	90.4%
	midparent (intrcpt)	0.494	0.395	0.594	0
	hybLS	-0.142	-0.150	-0.134	0							-21.69 % larger
	hybSL	-0.190	-0.198	-0.182	0							-28.36 % larger

Univarate meta-analysis for each cross

Quantify and visualise overall trend of phenotypic mean of hybrids compared to parents
Meta-analysis was repeated for subset data - including only non-novel phenotypes, only insects, and non-novel phenotypes in insects
Meta-analysis was conducted by using rma.mv function in metafor package

*Confidential interval : thick line

*Prediction interval : thin line

cap <- data.frame(
  Description = c(
    "Orchard plot for full data \n(Alltraits alltaxon)", 
    "Orchard plot for subset data:\n Excluding vertebrates \n(Alltraits insect)",
    "Orchard plot for subset data:\n Including all taxon but excluding novel phenotype \n(Nonnoveltraits alltaxon)",
    "Orchard plot for subset data:\n Excluding vertebrates and novel phenotype\n (Nonnoveltraits insect)"
    )
  ) %>%
  mutate_all(as.character)
row.names(cap) <- c("Alltraits.alltaxon", "Alltraits.insect", 
             "Nonnoveltraits.alltaxon", "Nonnoveltraits.insect")

for (tr in c("Alltraits", "Nonnoveltraits")) {
  for (taxon in c("alltaxon", "insect")) {
    
    if (taxon == "alltaxon") {
      dat <- Novel.Nonnovel %>%
        filter(cross == cr, data.type == tr)
    } else {
      dat <- Novel.Nonnovel %>%
        filter(cross == cr, data.type == tr, insect == taxon)
    }
    
    res.ma <- foreach(
      cr = c("spL", "hybLS", "hybSL"), .combine = `rbind`
      ) %do% {
      # # Phylogenetic random meta-analysis
      # phyl.random <- rma.mv(
      #   yi = lnRR.es, V = lnRR.sv,
      #   data = dat, method = "REML",
      #   random = list(
      #     ~1 | spL.name, ~1 | Study.ID, ~1 | Cross.ID, ~1 | ES.ID
      #     ),
      #   R = list(spL.name = phylo_cor)
      #   )
      # saveRDS(phyl.random,
      #   file = paste("../Analysis/Mean.meta", tr, taxon, cr, "obj", sep = "."))

      # creating summary table of results
      readRDS(
        paste("../Analysis/Mean.meta", tr, taxon, cr, "obj", sep = ".")
        ) %>%
          get_pred() %>%
          as.data.frame() %>%
          mutate(
            N = length(dat$ES.ID),
            # I2 = I2(phyl.random)["I2_total"] %>% round(3),
            cross = cr # indicate cross (spL, hybLS, hybSL)
            ) %>% 
        as.data.frame() %>%
        within('Dataset' <- c(paste(tr, taxon), "", "")) %>%  # dataset name in first row
        return()
        }
    
    if (taxon == "alltaxon") {
      dat.plot <- Novel.Nonnovel %>% 
        filter(data.type == tr)
    } else {
      dat.plot <- Novel.Nonnovel %>% 
        filter(data.type == tr, insect == taxon)
    }

    # Bind result of all crosses at each dataset
    assign(
      paste("res.ma", tr, taxon, sep = "."),
      res.ma
    )
    
    ## Midparent estimate & CI ###
    midparent.ma.2 <- readRDS(
      paste("../Analysis/Mean.midparent", tr, taxon, "obj", sep = ".")
      ) %>% 
      # Prediction interval of midparent
      get_pred() %>%
      as.data.frame()
    
    # creating a forest plot
    plot.metamean <- ggplot(
      data = res.ma, 
      aes(x = tanh(Estimate), y = cross)
      ) +
      scale_y_discrete(expand = c(0, 1)) +
      scale_x_continuous(
        limits = c(-1, 1), 
        breaks = seq(-1, 1, by = 0.5)
        ) + 
      ## Midparent CI ###
      geom_rect(
        aes(
          xmin = tanh(midparent.ma.2$LowerCI),
          xmax = tanh(midparent.ma.2$UpperCI),
          ymin = -Inf, ymax = Inf
          ),
        fill="Grey 90"
        ) +
      ## Midparent estimate ###
      geom_vline(
        xintercept = tanh(midparent.ma.2$Estimate), colour = "grey20", 
        alpha = 0.3, size = 1
        ) +
      ## Orchard plot ###
      geom_quasirandom(
        data = dat.plot, 
        aes(x = tanh(lnRR.es), y = cross, size = 1/lnRR.sv, color = cross), 
        alpha = 0.2, GroupOnX = FALSE
        ) + 
      scale_color_manual(values = c("#DC267F", "#785EF0", "#FE6100")) +
      # Delete legend for colors
      guides(fill = "none", colour = "none") +
      # Confidential interval : thicker line
      geom_errorbarh(
        aes(xmin = tanh(LowerCI), xmax = tanh(UpperCI)), 
        height = 0, size = 1.2, alpha = 0.6
        ) + 
      # Prediction interval : thinner line
      geom_errorbarh(
        aes(xmin = tanh(lowerPR), xmax = tanh(upperPR)), 
        height = 0.1, size = 0.5, alpha = 0.6
        ) +
      geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.3) +
      ## Estimate ###
      geom_point(size = 3, shape = 21, fill = "black") + 
      # ## I^2 ###
      # annotate(
      #   "text", x = -0.93, y = c(1.35, 2.35, 3.35),
      #   label = paste("italic(I^2)==", res.ma$I2), 
      #   parse = TRUE, hjust = "left", size = 5
      # ) + 
      ## N ###
      annotate(
        "text", x = -0.93, y = 3.85,
        label = paste("italic(N)==", length(dat.plot$ES.ID)/3), 
        parse = TRUE, hjust = "left", size = 5
      ) + 
      annotate(
        "text", x = -0.68, y = 3.85,
        label = "each", hjust = "left", size = 5
      ) + 
      ylab("") +
      xlab(expression(
        paste("hyperbolic tangent of lnRR from ", italic("spSS")), 
        parse = TRUE
        )) +
      ggtitle(cap[paste(tr, taxon,sep="."),]) +
      orchardtheme 

    print(plot.metamean)
  
    # ggsave(
    #   plot = plot.metamean,
    #   file = paste("../Analysis/mean.general.metamean", tr, taxon, "svg", sep = "."),
    #   height = 4, width = 5
    #   )
    
  }
}

bind_rows(
  res.ma.Alltraits.alltaxon, res.ma.Nonnoveltraits.alltaxon,
  res.ma.Alltraits.insect, res.ma.Nonnoveltraits.insect
  ) %>%
  as.data.frame() %>%
  # mutate_at(vars('Estimate':'%I2[residual]'), as.numeric) %>%
  # select(Dataset, cross, Estimate, UpperCI, LowerCI, contains("PR"), contains("I2")) %>%
  kable("html", digits = 3, caption = "Meta-analyses results of full dataset and data subsets") %>% 
  kable_styling("striped", position = "left") %>%
  scroll_box(width = "100%", height = "500px")

Meta-analyses results of full dataset and data subsets

Dataset	Fixed effects	Estimate	LowerCI	UpperCI	lowerPR	upperPR	P	I2[total]	%I2[species with phylogeny]	%I2[study]	%I2[crossed strain]	%I2[residual]	significance	N	cross
Alltraits alltaxon	intrcpt	0.837	0.411	1.263	-0.552	2.226	0.000	99.9%	28.2%	7.3%	0%	64.4%		0	spL
Alltraits alltaxon	intrcpt	0.477	0.246	0.708	-0.428	1.383	0.000	99.7%	16.8%	2.2%	0%	80.7%		0	hybLS
Alltraits alltaxon	intrcpt	0.425	0.178	0.672	-0.587	1.437	0.001	99.8%	14.9%	2.5%	0%	82.4%		0	hybSL
Nonnoveltraits alltaxon	intrcpt	0.983	0.555	1.411	-0.425	2.391	0.000	99.9%	24.5%	16.1%	0%	59.3%		79	spL
Nonnoveltraits alltaxon	intrcpt	0.482	0.257	0.708	-0.316	1.280	0.000	99.6%	21%	0%	0%	78.6%		79	hybLS
Nonnoveltraits alltaxon	intrcpt	0.421	0.210	0.633	-0.299	1.142	0.000	99.4%	22.3%	4.2%	0%	73%		79	hybSL
Alltraits insect	intrcpt	0.604	0.446	0.762	-0.486	1.693	0.000	99.9%	0%	12.3%	0%	87.6%		92	spL
Alltraits insect	intrcpt	0.362	0.265	0.460	-0.411	1.135	0.000	99.6%	0%	3.8%	0%	95.9%		92	hybLS
Alltraits insect	intrcpt	0.298	0.193	0.402	-0.583	1.178	0.000	99.7%	0%	2.1%	0%	97.6%		92	hybSL
Nonnoveltraits insect	intrcpt	0.741	0.541	0.940	-0.384	1.866	0.000	99.8%	0%	23.3%	0%	76.6%		72	spL
Nonnoveltraits insect	intrcpt	0.367	0.280	0.455	-0.271	1.005	0.000	99.4%	0%	2%	0%	97.4%		72	hybLS
Nonnoveltraits insect	intrcpt	0.313	0.225	0.401	-0.243	0.869	0.000	99.2%	0%	9.4%	0%	89.8%		72	hybSL

Crossing direction effect in phenotypic means

We statistically compared lnRR from spSS_M to the other crosses through the formal meta-regression by using rma.mv function of R package metafor.

Here we asked if lnRR of hybSL_M was smaller/larger than that of hybLS_M

If \(\ln\frac{hybridLS_M}{spSS_M}>\ln\frac{hybridSL_M}{spSS_M}\), hybridLS_M has larger phenotypic mean than hybridSL_M …maternal inheritance

for (tr in c("Alltraits", "Nonnoveltraits")) {
  for (taxon in c("alltaxon", "insect")) {
    
    if (taxon == "alltaxon") {
      dat <- Novel.Nonnovel %>% 
        filter(data.type == tr)
    } else {
      dat <- Novel.Nonnovel %>% 
        filter(data.type == tr, insect == taxon)
    }
  
    # #+++++++++++++++++++++++++++++++++++++++++++++#
    # # phylogenetic random regresson (ANOVA)
    # #+++++++++++++++++++++++++++++++++++++++++++++#
    # # Regress by lnRR of phenotypic difference between parentals
    # phyl.random.spL <- rma.mv(
    #   yi = lnRR.es, V = lnRR.sv,
    #   data = dat, method = "REML",
    #   random = list(~1 | spL.name, ~1 | Study.ID, ~1 | Cross.ID, ~1 | ES.ID),
    #   R = list(spL.name = phylo_cor),
    #   mods = ~ relevel(cross, ref = "spL")
    #   )
    # phyl.random.hybLS <- rma.mv(
    #   yi = lnRR.es, V = lnRR.sv,
    #   data = dat, method = "REML",
    #   random = list(~1 | spL.name, ~1 | Study.ID, ~1 | Cross.ID, ~1 | ES.ID),
    #   R = list(spL.name = phylo_cor),
    #   mods = ~ cross
    #   )
    # # Save model ###
    # saveRDS(
    #   phyl.random.spL,
    #   file = paste("../Analysis/Mean.compare.from.spL", tr, taxon, "obj", sep = ".")
    #   )
    # saveRDS(
    #   phyl.random.hybLS,
    #   file = paste("../Analysis/Mean.compare.from.hybLS", tr, taxon, "obj", sep = ".")
    #   )
    
    phyl.random.hybLS <- readRDS(
      paste("../Analysis/Mean.compare.from.hybLS", tr, taxon, "obj", sep = ".")
      ) 

    assign(
      paste("Reciprocal", tr, taxon, sep = "."),
      phyl.random.hybLS %>% 
        get_reg() %>%
        # Show dataset name
        within(Dataset <- c(paste(tr, taxon), rep("", length(.$Estimate)-1))) %>%
        # Rename fixed effects
        within('Fixed effects' <- c("", "hybLS (intrcpt)", "hybSL", "spLL")) %>%
        # Difference of each cross from midparent in %
        mutate('Comparison with hybridLS' = c(
          rep("", 2),
          paste(
            round(
              (exp(phyl.random.hybLS$beta[1]+phyl.random.hybLS$beta[2]) -
                 exp(phyl.random.hybLS$beta[1])
               )*100,
              2),
            "% larger"
            ), 
          ""
          )
        )
      )
  }
}

bind_rows(
  Reciprocal.Alltraits.alltaxon, Reciprocal.Nonnoveltraits.alltaxon,
  Reciprocal.Alltraits.insect, Reciprocal.Nonnoveltraits.insect
  ) %>%
  kable("html", digits = 3, caption = "Table S4. Results of meta-analysis investigating crossing direction effects in phenotypic means using full dataset or data subsets") %>% 
  kable_styling("striped", position = "left") %>%
  scroll_box(width = "100%", height = "500px")

Table S4. Results of meta-analysis investigating crossing direction effects in phenotypic means using full dataset or data subsets

Dataset	Fixed effects	Estimate	LowerCI	UpperCI	P	I2[total]	%I2[species with phylogeny]	%I2[study]	%I2[crossed strain]	%I2[residual]	significance	Comparison with hybridLS
Alltraits alltaxon						99.8%	39.1%	2.9%	0%	57.9%
	hybLS (intrcpt)	0.566	0.193	0.939	0.003
	hybSL	-0.028	-0.035	-0.022	0.000							-4.94 % larger
	spLL	0.240	0.233	0.246	0.000
Nonnoveltraits alltaxon						99.8%	35.7%	2.2%	0%	61.9%
	hybLS (intrcpt)	0.589	0.223	0.954	0.002
	hybSL	-0.049	-0.057	-0.041	0.000							-8.67 % larger
	spLL	0.368	0.360	0.376	0.000
Alltraits insect						99.7%	0%	11.9%	0%	87.8%
	hybLS (intrcpt)	0.349	0.239	0.460	0.000
	hybSL	-0.029	-0.036	-0.022	0.000							-4.08 % larger
	spLL	0.239	0.233	0.245	0.000
Nonnoveltraits insect						99.6%	0%	9.7%	0%	89.9%
	hybLS (intrcpt)	0.365	0.246	0.484	0.000
	hybSL	-0.051	-0.059	-0.043	0.000							-7.12 % larger
	spLL	0.368	0.361	0.376	0.000

Meta-analysis for phenotypic variation (coefficient of variation - CV)

We created data subset according to taxon (all taxon or insect only) and novelty in phenotypic variabilities (all traits or non-novel traits - trait variability [in terms of coefficient of variation] of hybrids does not exceed size range of parents). We conduct identical analyses for full data and data subsets, and checked robustness of our results.

Effect size calculation

We quantified relative phenotypic variability of hybrids and the parent with large variability (spLL_V) to the parent with small variability (spSS_V), by using [lnCVR] (https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.12309) (Nakagawa et al. (2015)). We controlled mean-variance relationship by using Coefficient of Variation (CV) as the proxy of phenotypic variability.
Effecct size was calculated by using escalc function in metafor package

# load spLS files
dat <- read.csv("../data/dat.CV.csv", head = TRUE)

# Order by taxon
dat$taxa <- ordered(
  dat$taxa,
  levels = c("Neuroptera", "Coleoptera", "Diptera", "Lepidoptera", "Orthoptera", "Aves", "Rodentia", "Anura", "Cichliformes")
  )

var.dif <- foreach(
  cr = c("hybLS", "hybSL", "spL"), 
  .combine = `rbind`
  ) %do% {
    escalc(
      measure = "CVR",
      # N of hybrids
      n1i = dat[, paste("N", cr, sep = ".")],
      # N of parentals
      n2i = dat[, "N.spS"],
      # Mean of hybrids
      m1i = dat[, paste("Mn", cr, sep = ".")],
      # Mean of parentals
      m2i = dat[, "Mn.spS"],
      # SD of hybrids
      sd1i = dat[, paste("SD", cr, sep = ".")],
      # SD of parentals
      sd2i = dat[, "SD.spS"],
      data = dat
      ) %>%
    rename(lnCVR.es = yi, lnCVR.sv = vi) %>%
    mutate(cross = cr)
  } %>% 
  drop_na(lnCVR.es) %>%
  arrange(ES.ID) %>%
  left_join(., dat)

Funnel plots

res <- rma(yi = lnCVR.es, vi = lnCVR.sv, data = var.dif, method="FE")

## set up 2x2 array for plotting
par(mfrow=c(2,2))
## draw funnel plots
funnel(res, main="Standard Error", xlim = c(-5, 5))
funnel(res, yaxis="vi", main="Sampling Variance", xlim = c(-5, 5))
funnel(res, yaxis="seinv", main="Inverse Standard Error", xlim = c(-5, 5))
funnel(res, yaxis="vinv", main="Inverse Sampling Variance", xlim = c(-5, 5))

Check data

Checked effect sizes with extremely high/low standard error (SE > 1.2 | 1/SE > 8.5) and confirmed that those data were correctly imported.

outliers <- var.dif %>%
  # vi (sampling variance) 
  mutate(SE = sqrt(lnCVR.sv)) %>%
  arrange(desc(SE)) %>%
  filter(SE > 1.2 | 1/SE > 8.5) %>%
  select(ES.ID, Study.name, SE, cross, Reciprocal, trait, Cross.ID) %>%
  mutate_if(is.numeric, round, 3)

outliers %>%
  kable("html", digits = 3, caption = "Effect sizes with extremely high/low standard error (SE > 1.2 | 1/SE > 140)") %>% 
  kable_styling("striped", position = "left") %>%
  scroll_box(width = "100%", height = "500px")

Effect sizes with extremely high/low standard error (SE > 1.2 | 1/SE > 140)

ES.ID	Study.name	SE	cross	Reciprocal	trait	Cross.ID
ES320	Musolf et al_2015	21.540	spL	Inviable	Frequ Modulated Downsweep_FAC1_2	musculus.3
ES253	Musolf et al_2015	10.257	hybSL	Inviable	Complex 2_FAC2_1	musculus.3
ES253	Musolf et al_2015	9.917	spL	Inviable	Complex 2_FAC2_1	musculus.3
ES147	Musolf et al_2015	6.096	spL	Inviable	U Shaped Inverted_FAC1_2	musculus.1
ES123	Musolf et al_2015	1.974	hybSL	Inviable	Complex 2_FAC2_1	musculus.1
ES320	Musolf et al_2015	1.933	hybLS	Inviable	Frequ Modulated Downsweep_FAC1_2	musculus.3
ES367	Suvanto et al_1994	1.656	hybSL	Viable	inhbt_IPI	Drosophila_borealis_Drosophila_virilis
ES371	Suvanto et al_1994	1.617	spL	Inviable	inhbt_PL	Drosophila_montana_Drosophila_virilis
ES125	Musolf et al_2015	1.376	spL	Inviable	Complex 2_FAC1_4	musculus.1
ES123	Musolf et al_2015	1.334	spL	Inviable	Complex 2_FAC2_1	musculus.1
ES254	Musolf et al_2015	1.265	spL	Inviable	Complex 2_FAC1_3	musculus.3
ES246	Musolf et al_2015	1.216	spL	Inviable	Complex 2_bandwcentre	musculus.3
ES367	Suvanto et al_1994	1.205	spL	Viable	inhbt_IPI	Drosophila_borealis_Drosophila_virilis
ES388	Tomaru&Oguma_1994	0.104	hybLS	Inviable	IPI	Drosophila_triauraria_Drosophila_biauraria
ES041	Coyne_1985	0.087	hybSL	Viable	Sex comb tooth number	BGxOxnard
ES387	Tomaru&Oguma_1994	0.076	spL	Viable	IPI	Drosophila_triauraria_Drosophila_quadraria
ES390	Tomaru&Oguma_1994	0.075	hybLS	Inviable	IPI	Drosophila_quadraria_Drosophila_subauraria
ES390	Tomaru&Oguma_1994	0.073	spL	Inviable	IPI	Drosophila_quadraria_Drosophila_subauraria
ES041	Coyne_1985	0.071	spL	Viable	Sex comb tooth number	BGxOxnard
ES041	Coyne_1985	0.071	hybLS	Viable	Sex comb tooth number	BGxOxnard
ES389	Tomaru&Oguma_1994	0.071	spL	Viable	IPI	Drosophila_quadraria_Drosophila_biauraria
ES388	Tomaru&Oguma_1994	0.064	spL	Inviable	IPI	Drosophila_triauraria_Drosophila_biauraria
ES391	Tomaru&Oguma_1994	0.060	spL	Viable	IPI	Drosophila_biauraria_Drosophila_subauraria

var.dif.2 <- var.dif %>%
  filter(!ES.ID %in% unique(outliers$ES.ID)) 
write.csv(var.dif.2, "../data/variation.ES.general.csv", row.names = F)

Because they inhibit meta-analytic model convergence, we excluded these outliers from the formal meta-analysis dataset, but not from novel variability dataset.

Funnel plots again

Funnel plots after outliers excluded

res <- rma(yi = lnCVR.es, vi = lnCVR.sv, data = var.dif.2, method="FE")
## set up 2x2 array for plotting
par(mfrow=c(2,2))
## draw funnel plots
funnel(res, main="Standard Error", xlim = c(-5, 5))
funnel(res, yaxis="vi", main="Sampling Variance", xlim = c(-5, 5))
funnel(res, yaxis="seinv", main="Inverse Standard Error", xlim = c(-5, 5))
funnel(res, yaxis="vinv", main="Inverse Sampling Variance", xlim = c(-5, 5))

Jugding novelty in phenotypic variabilities (using CV)

# load spLS files
dat <- read.csv("../data/dat.CV.csv", head = TRUE)

#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#
# Difference between larger species (spL) and hybrids 
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#

for (i in c("spL")) { # identify type of hybrids
  for (cr in c("hybLS", "hybSL")) { # identify type of parentals
    assign(
      paste(cr, i, sep = "_"),
        escalc(
          measure = "CVR",
          # N of hybrids
          n1i = dat[, paste("N", i, sep = ".")],
          # N of parentals
          n2i = dat[, paste("N", cr, sep = ".")],
          # Mean of hybrids
          m1i = dat[, paste("Mn", i, sep = ".")],
          # Mean of parentals
          m2i = dat[, paste("Mn", cr, sep = ".")],
          # SD of hybrids
          sd1i = dat[, paste("SD", i, sep = ".")],
          # SD of parentals
          sd2i = dat[, paste("SD", cr, sep = ".")],
          data = dat
          ) %>%
          select(ES.ID, yi, vi) %>%
          rename("lnCVR.es" = "yi", "lnCVR.sv" = "vi") %>% 
      mutate(
        # direction of transgressve segregation - greater than parent
        direction = "+",
        # SE for logistic distribution, for the weighted binomial regression
        # SE = pi/sqrt(3*(n_e + n_c))
        SE = pi/sqrt(3*(
          dat[, paste("N", i, sep = ".")] + dat[, paste("N", cr, sep = ".")]
        ))
      )
    )
  }
}

#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#
# Difference between hybrids and smaller species (spS)
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#

for (i in c("hybLS", "hybSL")) { # identify type of hybrids
  for (cr in c("spS")) { # identify type of parentals
    assign(
      paste(i, cr, sep = "_"),
        escalc(
          measure = "CVR",
          # N of hybrids
          n1i = dat[, paste("N", i, sep = ".")],
          # N of parentals
          n2i = dat[, paste("N", cr, sep = ".")],
          # Mean of hybrids
          m1i = dat[, paste("Mn", i, sep = ".")],
          # Mean of parentals
          m2i = dat[, paste("Mn", cr, sep = ".")],
          # SD of hybrids
          sd1i = dat[, paste("SD", i, sep = ".")],
          # SD of parentals
          sd2i = dat[, paste("SD", cr, sep = ".")],
          data = dat
          ) %>%
          select(ES.ID, yi, vi) %>%
          rename("lnCVR.es" = "yi", "lnCVR.sv" = "vi") %>% 
      mutate(
        # direction of transgressve segregation - smaller than parent
        direction = "-",
        # SE for logistic distribution, for the weighted binomial regression
        # SE = pi/sqrt(3*(n_e + n_c))
        SE = pi/sqrt(3*(
          dat[, paste("N", i, sep = ".")] + dat[, paste("N", cr, sep = ".")]
        ))
      )
    )
  }
}


#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#
# Data output
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#

bind_rows(
  ## Difference with mother species ###
  bind_rows(hybLS_spL, hybSL_spS) %>%
    mutate(parental = "Mother"),
  ## Difference with father species ###
  bind_rows(hybLS_spS, hybSL_spL) %>%
    mutate(parental = "Father")
  ) %>%
  drop_na(lnCVR.es) %>%
  left_join(., dat) %>%
  # Assign novel variability to 1, non-novel variability to 0 for each effect size
  mutate(Novelty = ifelse(lnCVR.es < 0, 1, 0)) %>%
  ## Join with metadata ###
  left_join(
    .,
    read.xlsx(
      "../data/original.data.xlsx", sheet = "Species.level.moderators"
      )
    ) %>%
  mutate_at(vars(contains("divergence")), as.numeric) %>%
  mutate_at(vars(contains("divergence")), scale) %>%
  as.data.frame %>%
  select(-trait) %>%
  drop_na(Hetero.sex, trait.type, Genet.divergence) %>%
  # order taxon
  within(
    taxa <- ordered(
      taxa,
      levels = c("Neuroptera", "Coleoptera", "Diptera", "Lepidoptera", "Orthoptera", "Aves", "Rodentia", "Anura", "Cichliformes")
      )
    ) %>%
  mutate_if(is.character, as.factor) %>%
  # Insect or no
  mutate(
    insect = ifelse(taxa %in% c("Aves", "Rodentia", "Anura", "Cichliformes"), "no", "insect"),
  ) %>%
  write.csv("../data/variation.ES.Novelty.csv", row.names = F)

Reload data & Prepare phylogenetic tree

# Load effect sizes
var.dif <- read.csv("../data/variation.ES.general.csv", head = TRUE)%>%
  # indicate dataset including novel variability
  mutate(data.type = "Alltraits")

# Observations with novel variability
All <- read.csv("../data/variation.ES.novelty.csv", head = TRUE) %>%
  filter(lnCVR.es < 0) %>%
  distinct(ES.ID)
# Effect sizes with non-novel variability
Nonovel <- read.csv("../data/variation.ES.general.csv", head = TRUE) %>%
  filter(!ES.ID %in% unique(All$ES.ID)) %>%
  # indicate dataset without novel variability
  mutate(data.type = "Nonnoveltraits")

# combine dataset with/without novel variability
Novel.Nonnovel <- bind_rows(var.dif, Nonovel) %>%
  mutate(metaunit =  str_c(data.type, cross, sep = "_")) %>%
  # Use observations with both reciprocal crosses
  drop_na(contains("Mn"), contains("SD")) %>%
  # Insect or no
  mutate(
    insect = ifelse(taxa %in% c("Cichliformes", "Anura", "Aves", "Rodentia"), "no", "insect"),
  )

#+++++++++++++++++++++++++++++++#
# Phylogeny 
#+++++++++++++++++++++++++++++++#

# # matching names from open tree taxonomy
# taxa <- tnrs_match_names(
#   names = levels(var.dif$spL.name) %>%
#     str_replace_all("_", " "), 
#   context_name = "Animals"
#   )
# 
# # Create tree
# tree <- tol_induced_subtree(ott_ids = taxa$ott_id)
# tree$tip.label %<>%
#   strip_ott_ids # remove OTT IDs from tip labels
# # randomly solve non-binary phylogeny
# set.seed(6)
# bin.tree <- multi2di(tree, random = T)
# 
# # Fix names of tip labels
# bin.tree$tip.label %<>% str_replace_all("Dryophytes", "Hyla")
# # Indicate mismatch between tip labels & dataset species names
# setdiff(levels(as.factor(bin.tree$tip.label)), levels(var.dif$spL.name))
# setdiff(levels(var.dif$spL.name), levels(as.factor(bin.tree$tip.label)))

# write.tree(bin.tree, file= "../data/phylo.variation.tre")

# compute branch lengths of tree
phylo_branch <- read.tree(file = "../data/phylo.variation.tre") %>%
  compute.brlen(bin.tree, method = "Grafen", power = 1)

# saving phylogeneic matrix
phylo_cor <- vcv(phylo_branch, cor = T)

# Note one of the tips is called "Lates calcarifer (estimated)"
phylo_branch$node.label <- NULL

# Plot tree
plot.phylo(phylo_branch, cex = 0.7)

Figure S2. Phylogenetic tree used in meta-analysis for phenotypic variability (coefficient of variation, CV), which was based on tree of the parental species with larger CV in trait size

Dominance in coefficient of variation (CV)

We compared midparent-value of phenotypic variability (CV) and hybrids’ phenotypic variability by calculating log ratio of phenotypic variability (lnCVR) of midparent-value and hybrids to spSS_V.

lnCVR of midparent-value to spSS_V was, following \(lnCVR=\ln{\frac{CV_E}{CV_C}}+\frac{1}{2(N_E-1)}-\frac{1}{2(N_C-1)}\) , calculated as
\[\ln\frac{CV_{spLL_V}+CV_{spSS_V}}{2}-\ln{CV_{spSS_V}} + \frac{1}{N_{spLL_V}+N_{spSS_V}-2}-\frac{1}{2(N_{spSS_V} - 1)}\] It’s sampling variance was substitute by that of spLL_V

We statistically compared those effect sizes through the formal meta-regression by using rma.mv function of R package metafor

All meta-analytic models included following Random effects estimates:

• Study.ID: Primary studies. Denoted as Study

• Cross.ID: Parental strain used in the crossing. Discriminate intraspecific populations. Denoted as crossed strain

• spL.name: Phylogeny of parental species (spLL_M). Denoted as species with phylogeny

#+++++++++++++++++++++++++++++++++++++++++++#
# Calculate midparent 
#+++++++++++++++++++++++++++++++++++++++++++#
midparent <- Novel.Nonnovel %>%
  # Divide mean by 2 in spL whereas hybrids are constant
   mutate(
     CV.spL = SD.spL/Mn.spL,
     CV.spS = SD.spS/Mn.spS
     ) %>%
   mutate(
     lnCVR.midparent.es =
       ifelse(
         cross == "spL",
         # log(CVe/CVc)
         log((CV.spL + CV.spS)/2) - log(CV.spS) +  
           # 1/2(Ne-1) - 1/2(Nc-1)
           1/(N.spL + N.spS -2) - 1/(2*N.spS - 2), 
         lnCVR.es
         )
     # Divide variance by 4 in spL whereas hybrids are constant
     ) %>%
   mutate_at("cross", as.factor) %>%
   within(levels(cross) <- c("hybLS", "hybSL", "midparent"))


#+++++++++++++++++++++++++++++++++++++++++++++++++++#
# Compare midparent and hybrids by meta-regresson
#+++++++++++++++++++++++++++++++++++++++++++++++++++#

for (tr in c("Alltraits", "Nonnoveltraits")) {
  for (taxon in c("alltaxon", "insect")) {
    
    # Filtering taxon (all taxon or insects)
    if (taxon == "alltaxon") {
      dat <- midparent %>% 
        filter(data.type == tr)
    } else {
      dat <- midparent %>% 
        filter(data.type == tr, insect == taxon)
    }

    # #+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++#
    # # Estimate meta-analytic mean of midparent of phenotypic variation
    # #+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++#
    # midparent.compare <- rma.mv(
    #   yi = lnCVR.midparent.es, V = lnCVR.sv,
    #   data = dat,
    #   method = "REML",
    #   random = list(~1 | spL.name, ~1 | Study.ID, ~1 | Cross.ID, ~1 | ES.ID),
    #   R = list(spL.name = phylo_cor),
    #   mods = ~ relevel(cross, ref = "midparent")
    #   )
    # ## Save model ###
    # saveRDS(
    #   midparent.compare,
    #   file = paste("../Analysis/Variation.midparent.compare", tr, taxon, "obj", sep = ".")
    #   )
  
    ## Load model ###
    midparent.compare <- readRDS(paste("../Analysis/Variation.midparent.compare", tr, taxon, "obj", sep = "."))
    
    assign(
      paste("Dominance", tr, taxon, sep = "."),
      get_reg(midparent.compare) %>%
        # Show dataset name
        within(Dataset <- c(paste(tr, taxon), rep("", length(.$Estimate)-1))) %>%
        # Rename fixed effects
        within('Fixed effects' <- c("", "midparent (intrcpt)", "hybLS", "hybSL")) %>%
        # Difference of each cross from midparent in %
        mutate('Comparison with midparent' = c(
          rep("", 2),
          # Difference hybLS - midparent
          paste(
            round(
            100*(exp(midparent.compare$beta[1]+midparent.compare$beta[2])            -exp(midparent.compare$beta[1])), # hybrid LS
            2),
            "% larger"
            ),
              # beta[1] : midparent
              # beta[2] : hybLS
              # beta[3] : hybSL
          # Difference hybSL - midparent
          paste(
            round(
            100*(exp(midparent.compare$beta[1]+midparent.compare$beta[3])-exp(midparent.compare$beta[1])), # hybrid SL
            2),
            "% larger"
            )
          ))
      )

    # #++++++++++++++++++++++++++++++++++++++++++++++#
    # # Meta-analysis for midparent to plot band
    # #++++++++++++++++++++++++++++++++++++++++++++++#
    # 
    # # Phylogenetic random meta-analysis
    # midparent.random <- rma.mv(
    #   yi = lnCVR.midparent.es,
    #   V = lnCVR.sv,
    #   data = dat %>%
    #     filter(cross == "midparent"),
    #   method = "REML",
    #   random = list(~1 | spL.name, ~1 | Study.ID, ~1 | Cross.ID, ~1 | ES.ID),
    #   R = list(spL.name = phylo_cor)
    #   )
    # ## Save model ###
    # saveRDS(
    #   midparent.random,
    #   file = paste("../Analysis/Variation.midparent", tr, taxon, "obj", sep = ".")
    #   )
    
  }
}

bind_rows(
  Dominance.Alltraits.alltaxon, Dominance.Nonnoveltraits.alltaxon,
  Dominance.Alltraits.insect, Dominance.Nonnoveltraits.insect
  ) %>%
  kable("html", digits = 3, caption = "Table S5. Results of meta-analysis investigating dominance in phenotypic variabilities using full dataset or data subsets") %>%
  kable_styling("striped", position = "left") %>%
  scroll_box(width = "100%", height = "500px")

Table S5. Results of meta-analysis investigating dominance in phenotypic variabilities using full dataset or data subsets

Dataset	Fixed effects	Estimate	LowerCI	UpperCI	P	I2[total]	%I2[species with phylogeny]	%I2[study]	%I2[crossed strain]	%I2[residual]	significance	Comparison with midparent
Alltraits alltaxon						75.6%	19.5%	11.4%	0%	44.7%
	midparent (intrcpt)	0.381	0.079	0.682	0.013
	hybLS	-0.020	-0.098	0.058	0.610							-2.94 % larger
	hybSL	-0.091	-0.170	-0.013	0.022							-12.77 % larger
Nonnoveltraits alltaxon						69.9%	14.4%	21%	0%	34.5%
	midparent (intrcpt)	0.589	0.275	0.902	0.000
	hybLS	-0.146	-0.257	-0.035	0.010							-24.49 % larger
	hybSL	-0.144	-0.254	-0.033	0.011							-24.13 % larger
Alltraits insect						67.8%	0.1%	15.3%	0%	52.4%
	midparent (intrcpt)	0.261	0.118	0.404	0.000
	hybLS	-0.016	-0.096	0.064	0.695							-2.06 % larger
	hybSL	-0.113	-0.193	-0.032	0.006							-13.82 % larger
Nonnoveltraits insect						56.9%	0%	24.4%	0%	32.5%
	midparent (intrcpt)	0.452	0.259	0.645	0.000
	hybLS	-0.144	-0.258	-0.030	0.013							-21.11 % larger
	hybSL	-0.173	-0.287	-0.059	0.003							-24.94 % larger

Univarate meta-analysis for each cross (using CV)

Quantify and visualise overall trend of phenotypic variability (CV) of hybrids compared to parents
Meta-analysis was repeated for subset data - including only non-novel phenotypes, only insects, and non-novel phenotypes in insects
Meta-analysis was conducted by using rma.mv function in metafor package

• Confidential interval : thick line

• Prediction interval : thin line

for (tr in c("Alltraits", "Nonnoveltraits")) {
  for (taxon in c("alltaxon", "insect")) {

    if (taxon == "alltaxon") {
      dat <- Novel.Nonnovel %>%
        filter(cross == cr, data.type == tr)
    } else {
      dat <- Novel.Nonnovel %>%
        filter(cross == cr, data.type == tr, insect == taxon)
    }
    
    res.ma <- foreach(
      cr = c("spL", "hybLS", "hybSL"), .combine = `rbind`
      ) %do% {
      # # Phylogenetic random meta-analysis
      # phyl.random <- rma.mv(
      #   yi = lnCVR.es, V = lnCVR.sv,
      #   data = dat, method = "REML",
      #   random = list(~1 | spL.name, ~1 | Study.ID, ~1 | Cross.ID, ~1 | ES.ID),
      #   R = list(spL.name = phylo_cor)
      #   )
      # saveRDS(phyl.random,
      #   file = paste("../Analysis/Variation.meta", tr, taxon, cr, "obj", sep = "."))

      # creating summary table of results
      readRDS(
        paste("../Analysis/Variation.meta", tr, taxon, cr, "obj", sep = ".")
        ) %>%
          get_pred() %>%
          as.data.frame() %>%
          mutate(
            N = length(dat$ES.ID),
            # I2 = I2(phyl.random)["I2_total"] %>% round(3),
            cross = cr # indicate cross (spL, hybLS, hybSL)
            ) %>% 
        as.data.frame() %>%
        within('Dataset' <- c(paste(tr, taxon), "", "")) %>%  # dataset name in first row
        return()
        }

    if (taxon == "alltaxon") {
      dat.plot <- Novel.Nonnovel %>% 
        filter(data.type == tr)
    } else {
      dat.plot <- Novel.Nonnovel %>% 
        filter(data.type == tr, insect == taxon)
    }
    
    # Bind result of all crosses at each dataset
    assign(
      paste("res.ma", tr, taxon, sep = "."),
      res.ma
    )
    
    ## Midparent estimate & CI ###
    midparent.ma.2 <- readRDS(
      paste("../Analysis/Variation.midparent", tr, taxon, "obj", sep = ".")
      ) %>% 
      # Prediction interval of midparent
      get_pred() %>%
      as.data.frame()
    
    # creating a forest plot
    plot.metamean <- ggplot(
      data = res.ma,
      aes(x = tanh(Estimate), y = cross)
      ) +
      scale_y_discrete(expand = c(0,1)) +
      scale_x_continuous(
        limits = c(-1, 1),
        breaks = seq(-1, 1, by = 0.5)
        ) +
      ## Midparent CI ###
      geom_rect(
        aes(
          xmin = tanh(midparent.ma.2$LowerCI),
          xmax = tanh(midparent.ma.2$UpperCI),
          ymin = -Inf, ymax = Inf
          ),
        fill="Grey 95", inherit.aes = FALSE
        ) +
      ## Midparent estimate ###
      geom_vline(
        xintercept = tanh(midparent.ma.2$Estimate), colour = "grey20",
        alpha = 0.3, size = 1
        ) +
      ## Orchard plot ###
      geom_quasirandom(
        data = dat.plot %>%
          filter(data.type == tr),
        aes(x = tanh(lnCVR.es), y = cross, size = 1/lnCVR.sv, color = cross),
        alpha = 0.2, GroupOnX = FALSE
        ) +
      scale_color_manual(values = c("#DC267F", "#785EF0", "#FE6100")) +
      # Delete legend for colors
      guides(fill = "none", colour = "none") +
      ## Confidential interval ###
      geom_errorbarh(
        aes(xmin = tanh(LowerCI), xmax = tanh(UpperCI)),
        height = 0, size = 1.2, alpha = 0.6
        ) +
      ## Prediction interval ###
      geom_errorbarh(
        aes(xmin = tanh(lowerPR), xmax = tanh(upperPR)),
        height = 0.1, size = 0.5, alpha = 0.6
        ) +
      geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.3) +
      ## Estimate ###
      geom_point(size = 3, shape = 21, fill = "black") +
      ## N ###
      annotate(
        "text", x = -0.93, y = 3.85,
        label = paste("italic(N)==", length(dat.plot$ES.ID)/3),
        parse = TRUE, hjust = "left", size = 5
      ) +
      annotate(
        "text", x = -0.68, y = 3.85,
        label = "each", hjust = "left", size = 5
      ) +
      ylab("") +
      xlab(expression(
        paste("hyperbolic tangent of lnCVR from ", italic("spSS")),
        parse = TRUE
        )) +
      ggtitle(cap[paste(tr, taxon,sep="."),]) +
      orchardtheme 
    
    print(plot.metamean)

    # ggsave(
    #   plot = plot.metamean,
    #   file = paste("../Analysis/variation.general.metamean", tr, taxon, "svg", sep = "."),
    #   height = 4, width = 5
    # )

  }
}

bind_rows(
  res.ma.Alltraits.alltaxon, res.ma.Nonnoveltraits.alltaxon,
  res.ma.Alltraits.insect, res.ma.Nonnoveltraits.insect
  ) %>%
  as.data.frame() %>%
  select(Dataset, cross, Estimate, UpperCI, LowerCI, contains("PR"), contains("I2")) %>%
  kable("html", digits = 3, caption = "Meta-analyses results of full dataset and data subsets") %>% 
  kable_styling("striped", position = "left") %>%
  scroll_box(width = "100%", height = "500px")

Meta-analyses results of full dataset and data subsets

Dataset	cross	Estimate	UpperCI	LowerCI	lowerPR	upperPR	I2[total]	%I2[species with phylogeny]	%I2[study]	%I2[crossed strain]	%I2[residual]
Alltraits alltaxon	spL	0.538	0.763	0.314	-0.240	1.317	68.9%	12.7%	12.1%	0%	44.1%
Alltraits alltaxon	hybLS	0.302	0.531	0.073	-0.913	1.516	82.4%	3.2%	10.8%	0%	68.4%
Alltraits alltaxon	hybSL	0.345	0.790	-0.101	-1.048	1.738	84.8%	24.5%	18.4%	0%	41.8%
Nonnoveltraits alltaxon	spL	0.734	0.983	0.485	-0.265	1.733	77.2%	0%	29.1%	0%	48.1%
Nonnoveltraits alltaxon	hybLS	0.402	0.646	0.157	-0.567	1.371	69.6%	0%	24%	0%	45.7%
Nonnoveltraits alltaxon	hybSL	0.556	1.161	-0.049	-0.722	1.834	77%	66.9%	0%	0%	10.2%
Alltraits insect	spL	0.446	0.570	0.322	-0.181	1.073	61.4%	0%	14.2%	0%	47.3%
Alltraits insect	hybLS	0.226	0.403	0.048	-0.892	1.343	80.5%	0%	8.9%	0%	71.6%
Alltraits insect	hybSL	0.151	0.362	-0.061	-0.957	1.259	79.5%	0%	23.3%	0%	56.2%
Nonnoveltraits insect	spL	0.647	0.870	0.424	-0.185	1.479	70.2%	0%	24.3%	0%	45.9%
Nonnoveltraits insect	hybLS	0.317	0.543	0.091	-0.488	1.122	60.6%	0%	22.1%	0%	38.5%
Nonnoveltraits insect	hybSL	0.256	0.437	0.076	-0.222	0.735	33.5%	0%	21%	11%	1.5%

Crossing direction effect in phenotypic variability (CV)

We statistically compared lnCVR from spSS_M to the other crosses through the formal meta-regression by using rma.mv function of R package metafor.
Here we asked if lnCVR of hybSL_V was smaller/larger than that of hybLS_V. Smaller lnCVR of hybSL_V indicates maternal inheritance in phenotypic variability

for (tr in c("Alltraits", "Nonnoveltraits")) {
  for (taxon in c("alltaxon", "insect")) {
    
    if (taxon == "alltaxon") {
      dat <- Novel.Nonnovel %>% 
        filter(data.type == tr)
    } else {
      dat <- Novel.Nonnovel %>% 
        filter(data.type == tr, insect == taxon)
    }

  #++++++++++++++++++++++++++++++++++++++++++++++#
  # phylogenetic random regresson (ANOVA)
  #++++++++++++++++++++++++++++++++++++++++++++++#
    
  # # Regress by lnCVR of phenotypic difference between parentals
  # phyl.random.spL <- rma.mv(
  #   yi = lnCVR.es, V = lnCVR.sv,
  #   data = dat, method = "REML",
  #   random = list(~1 | spL.name, ~1 | Study.ID, ~1 | Cross.ID, ~1 | ES.ID),
  #   R = list(spL.name = phylo_cor),
  #   mods = ~ relevel(cross, ref = "spL")
  #   )
  # phyl.random.hybLS <- rma.mv(
  #   yi = lnCVR.es, V = lnCVR.sv,
  #   data = dat, method = "REML",
  #   random = list(~1 | spL.name, ~1 | Study.ID, ~1 | Cross.ID, ~1 | ES.ID),
  #   R = list(spL.name = phylo_cor),
  #   mods = ~ cross
  #   )
  # # Save model ###
  # saveRDS(
  #   phyl.random.spL,
  #   file = paste("../Analysis/Variation.compare.from.spL", tr, taxon, "obj", sep = ".")
  #   )
  # saveRDS(
  #   phyl.random.hybLS,
  #   file = paste("../Analysis/Variation.compare.from.hybLS", tr, taxon, "obj", sep = ".")
  #   )
    
    phyl.random.hybLS <- readRDS(
      paste("../Analysis/Variation.compare.from.hybLS", tr, taxon, "obj", sep = ".")
      )

  assign(
    paste("Reciprocal", tr, taxon, sep = "."),
    phyl.random.hybLS %>% 
      get_reg() %>%
      # Show dataset name
      within(Dataset <- c(paste(tr, taxon), rep("", length(.$Estimate)-1))) %>%
      # Rename fixed effects
      within('Fixed effects' <- c("", "hybLS (intrcpt)", "hybSL", "spLL")) %>%
      # Difference of each cross from midparent in %
      mutate('Comparison with hybridLS' = c(
        rep("", 2),
        paste(
          round(
            (exp(phyl.random.hybLS$beta[1]+phyl.random.hybLS$beta[2]) - exp(phyl.random.hybLS$beta[1]))*100,
            2),
          "% larger"
          ), 
        ""
        )
      )
    )
  }
}

bind_rows(
  Reciprocal.Alltraits.alltaxon, Reciprocal.Nonnoveltraits.alltaxon,
  Reciprocal.Alltraits.insect, Reciprocal.Nonnoveltraits.insect
  ) %>%
  kable("html", digits = 3, caption = "Table S6. Results of meta-analysis investigating cross direction effect in phenotypic variabilities using full dataset or data subsets") %>% 
  kable_styling("striped", position = "left") %>%
  scroll_box(width = "100%", height = "500px")

Table S6. Results of meta-analysis investigating cross direction effect in phenotypic variabilities using full dataset or data subsets

Dataset	Fixed effects	Estimate	LowerCI	UpperCI	P	I2[total]	%I2[species with phylogeny]	%I2[study]	%I2[crossed strain]	%I2[residual]	significance	Comparison with hybridLS
Alltraits alltaxon						78.2%	20.1%	9.6%	0%	48.5%
	hybLS (intrcpt)	0.377	0.055	0.700	0.022
	hybSL	-0.074	-0.155	0.006	0.070							-10.44 % larger
	spLL	0.190	0.112	0.268	0.000
Nonnoveltraits alltaxon						73.8%	10.2%	23%	0%	40.6%
	hybLS (intrcpt)	0.451	0.136	0.766	0.005
	hybSL	-0.002	-0.118	0.114	0.973							-0.31 % larger
	spLL	0.362	0.251	0.474	0.000
Alltraits insect						72.1%	0.8%	14.2%	0%	57.1%
	hybLS (intrcpt)	0.258	0.095	0.420	0.002
	hybSL	-0.100	-0.183	-0.016	0.019							-12.26 % larger
	spLL	0.178	0.098	0.258	0.000
Nonnoveltraits insect						65.2%	0%	24.7%	0%	40.5%
	hybLS (intrcpt)	0.329	0.107	0.550	0.004
	hybSL	-0.032	-0.153	0.088	0.598							-4.42 % larger
	spLL	0.354	0.240	0.468	0.000

Novelty in phenotypic means

Frequency

dat.full <- read.csv("../data/mean.ES.Novelty.csv", head = TRUE)

## N of novel phenotype in each taxa and each direction ###
summary <- bind_rows(
  # All observations
  dat.full %>%
    group_by(taxa) %>%
    summarise(
      'Species pair' = length(unique(species.pair)),
      Observations = length(unique(ES.ID)),
      Study = length(unique(Study.ID))
      ) %>%
    mutate(direction = "All Observations")
  ,
  # novel phenotype for any direction
  dat.full %>%
    filter(Novelty == "1") %>%
    group_by(taxa) %>%
    summarise(
      'Species pair' = length(unique(species.pair)),
      Observations = length(unique(ES.ID)),
      Study = length(unique(Study.ID))
      ) %>%
    mutate(direction = "Novel phenotypic means")
  ,
  # novel phenotype for each direction
  dat.full %>%
    filter(Novelty == "1") %>%
    group_by(taxa, direction) %>%
    summarise(
      'Species pair' = length(unique(species.pair)),
      Observations = length(unique(ES.ID)),
      Study = length(unique(Study.ID))
      )
  ) %>%
  # Assign 0 for non-novel phenotype
  replace(., is.na(.), "0") %>% 
  # Make tidy data
  gather(key = metrics, value = N, -c(taxa, direction)) %>%
  # Order factors
  mutate_at(vars("metrics", "direction"), as.factor) %>%
  filter(metrics != "Study")

# Order taxon
summary$metrics <- ordered(summary$metrics, levels = c("Study", "Species pair", "Observations"))
# Order novel phenotype category
summary$direction <- ordered(summary$direction, levels = c("All Observations", "Novel phenotypic means", "+", "-"))
# Change names of TS category to more intuitive name
levels(summary$direction) <- c("All Observations", "Novel phenotypic means", "Exceed upper range", "Exceed lower range")

## Plot ###
plot <- ggplot(
  # rename levels of factors to wrap label text
  summary %>%
    within(levels(direction) <- c("All Obser- vations", "Novel phenotypic means", "Exceed upper range", "Exceed lower range")) %>%
    within(levels(metrics) <- c("Study", "Species pair", "Obser- vations")), 
  aes(x = "", y = N, fill = taxa)) +
  geom_bar(width = 1, stat = "identity") +
  scale_fill_brewer(palette = "Paired") +
  facet_grid(
    scale = "free", metrics ~ direction,
    # strip text into two lines
    labeller = label_wrap_gen(width = 10)
    ) +
  xlab("") + ylab("") +
  stackedbartheme
plot +
  ggtitle("Taxonomic distribution of novel phenotypic means")

# ggsave(
#   plot = plot, 
#   file = "../Analysis/mean.TS.frequency.svg", height = 3.7, width = 5.0
#   )

## Total number of observations and percentage ###
summary %>%
  group_by(direction, metrics) %>%
  # Count number of observaitons and species pairs
  summarise(sum(N)) %>%
  spread(key = metrics, value = "sum(N)") %>%
  as.data.frame() %>%
  # Calculate percentage
  mutate(
    'Species pair %' = .[, "Species pair"]/.[1, "Species pair"]*100,
    'Observaton %' = Observations/.[1, "Observations"]*100
  ) %>%
  mutate_at(vars(contains("percent")), round, 2) %>%
  kable("html", digits = 3, caption = "Summary of the novel phenotypic means frequency") %>% 
  kable_styling("striped", position = "left")

Summary of the novel phenotypic means frequency

direction	Species pair	Observations	Species pair %	Observaton %
All Observations	34	332	100.000	100.000
Novel phenotypic means	22	143	64.706	43.072
Exceed upper range	15	71	44.118	21.386
Exceed lower range	11	74	32.353	22.289

Factors affecting phenotypic novelty

We checked robustness of results by conducting identical analysis for insect subset data (removing vertebrate data).

Model, which was ran using MCMCglmm function, included following Random effects estimates:

• Study.ID: Primary studies

• Cross.ID: Parental strain used in the crossing. Discriminate intraspecific populations

• spL.name: Phylogeny of parental species (spLL_M)

• SE.units: Sampling variance of effect size

The moderators were categorised into 3 subsets: main effects (labelled as novelty in phenotypic means), interaction terms with compared parental species (spLL vs. spSS, labelled as exceed upper range; mother species vs. father, labelled as exceed mother).

# Correcting estimate of binomial regression
c2 <- (16 * sqrt(3)/(15 * pi))^2


#++++++++++++++++++++++++++++++++++#
# Phylogenetic tree for mcmcglmm 
#++++++++++++++++++++++++++++++++++#
# compute branch lengths of tree
phylo_branch <- read.tree(file = "../data/phylo.mean.tre") %>%
  compute.brlen(method = "Grafen", power = 1)

# saving phylogeneic matrix
phylo_cor <- vcv(phylo_branch, cor = T)

# generating inverse phylogenetic matrix for MCMCglmm
phylo_branch$node.label <- NULL
phylo_MCMC <- MCMCglmm::inverseA(phylo_branch, nodes = "ALL", scale = TRUE)$Ainv

# Caption
cap <- data.frame(
  desc = c(
    "Table S7. Fixed effects estimates of Bayesian logistic model investigating factors affecting novelty in phenotypic means. Data of all taxon was used",
    "Fixed effects estimates: insect subset data"
    )
  ) %>%
  mutate_all(as.character)
row.names(cap) <- c("alltaxon", "insect")

for (taxon in c("alltaxon", "insect")) {
  
  if (taxon == "alltaxon") {
    dat <- dat.full
  } else {
    dat <- dat.full %>% 
      filter(insect == taxon)
  }

  # ## Setting prior to logistic regression ###
  # prior <- list(
  #   B=list(
  #     mu = rep(0, 17), # N of coefficient. (level -1) for categorical factors, 1 for continuous factors, 1 for intercept
  #     V = gelman.prior(~
  #       parental*Hetero.sex +
  #       parental*direction +
  #       direction*Genet.divergence + direction*Pheno.divergence +
  #       direction*Hetero.sex +
  #       direction*trait.type +
  #       direction*Distribution + direction*Reciprocal,
  #     data = dat, # formula and data
  #     scale=sqrt(pi^2/3+1))), # error distribution of logistic regression
  #   R=list(V=1,fix=1),
  #   G = list(
  #     G1 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
  #     G2 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
  #     G3 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
  #     G4 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000)
  #     )
  #   )
  # ## Phylogenetic binomial regression ###
  # phylmix <-MCMCglmm(
  #   fixed = Novelty ~
  #     parental*Hetero.sex + parental*direction +
  #     direction*Genet.divergence + direction*Pheno.divergence +
  #     direction*Hetero.sex + direction*trait.type +
  #     direction*Distribution + direction*Reciprocal,
  #   # idh(SE):units | weight by SE of effect size
  #   random = ~ Study.ID + Cross.ID + spL.name + idh(SE):units,
  #   family = "categorical",
  #   verbose = FALSE,
  #   ginverse = list(spL.name = phylo_MCMC),
  #   prior = prior,
  #   data = dat,
  #   nitt = 60000,  # Increase the number of iterations, default is 13000
  #   burnin = 5000  # Increase the number of burnin, default is 3000
  # )
  # phylmix$Sol <- phylmix$Sol/sqrt(1+c2)
  # ## Save model ###
  # saveRDS(phylmix, file = paste("../Analysis/Mean.novelty.allfactors", taxon, "obj", sep = "."))

  #+++++++++++++++++++++++#
  # Grouping variables
  #+++++++++++++++++++++++#
  # Load model
  phylmix <- readRDS(paste("../Analysis/Mean.novelty.allfactors", taxon, "obj", sep = "."))
  
  sum <- get_fixed.MCMCglmm(summary(phylmix)$solutions) %>%
      ## Grouping Factors ###
      mutate(
        Group = 
          ifelse(str_detect(Factors, "direction.:"), 
                 "Exceed upper range",
                 ifelse(str_detect(Factors, "parentalMother:"),
                        "Exceed mother",
                        ifelse(Factors == "Hetero.sexMale:direction+",
                               "Exceed upper range",
                               "Increase novelty"
                               )
                        )
                 )
      ) %>%
      mutate_at("Group", as.factor) %>%
      within(Group <- ordered(Group, c("Increase novelty", "Exceed upper range", "Exceed mother"))) %>%
      ## Change factor names ###
      within(
        Factors <- str_remove_all(
          Factors, "direction.:"
          ) %>%
          str_remove_all(., "parentalMother:") %>%
          str_remove_all(., ":direction.")
        ) %>%
      within(
        Factors <- factor(
          Factors, ordered = TRUE, 
          levels = c("parentalMother", "direction+", "trait.typesound", "Hetero.sexMale", "ReciprocalViable", "DistributionOverlap", "Pheno.divergence",  "Genet.divergence",  "(Intercept)")
          )
        )
  levels(sum$Factors) <- c("Exceed mother", "Exceed upper range", "Sound traits", "Male heterogametic", "Viable reciprocal hybrids", "Distribution overlap", "Phenotypic divergence", "Genetic divergence", "Intercept")

  ## Plot ###
  metaplot <- ggplot(sum, aes(x = post.mean, y = Factors)) +
    # Vertical line
    geom_vline(
      xintercept = 0, size = 0.2, 
      colour = "grey30", linetype = "dotted"         
      ) +
    # CI
    geom_errorbarh(
      aes(
        xmin = sum[, 'l-95% CI'], xmax = sum[, 'u-95% CI'],
        colour=significance 
        ), 
      height = .0001
      ) +
    # Color of plots and errorbars
    scale_colour_manual(values = c("grey60", "black")) +
    geom_point(size = 1, aes(colour = significance)) +
    scale_fill_manual(values = c("grey60", "black")) +
    # Title
    ggtitle(paste("Fixed effects estimates using", taxon, "data")) +
    ylab("") + xlab("Estimate with 95% CI") +
    # Combine different plots for main Factors and interactions
    facet_grid(
      Group~., scales = "free", space = "free",
      labeller = label_wrap_gen(width = 20)
      ) +
    # Themes
    foresttheme
print(metaplot)

  # ggsave(
  #     plot = metaplot,
  #     file = paste("../Analysis/mean.novelty.result", taxon, "svg", sep = "."),
  #     height = 4.7, width = 4.0
  #     )

  #+++++++++++++++++++++++++#
  # Fixed effects output
  #+++++++++++++++++++++++++#
  sum %>% 
    select(Group, Factors, Estimates, '95% credible interval', P, significance, Description) %>%
    kable("html", caption = cap[taxon,]) %>% 
    kable_styling("striped", position = "left") %>%
    scroll_box(width = "100%", height = "500px") %>%
    print()
  
  #+++++++++++++++++++++++++++#
  # Random effects output 
  #+++++++++++++++++++++++++++#
  # transformation for varaince
  get_random.MCMCglmm(summary(phylmix$VCV/(1+c2))) %>%
    kable("html", digits = 2,
          caption = paste("Random effects estimates: ", taxon, "data", " ")
          ) %>% 
    kable_styling("striped", position = "left") %>%
    print()

}

Table S7. Fixed effects estimates of Bayesian logistic model investigating factors affecting novelty in phenotypic means. Data of all taxon was used

Group	Factors	Estimates	95% credible interval	P	significance	Description
Increase novelty	Intercept	-1.82	-5.19 – 1.64	0.269		\(\beta\) = -1.82, CI = -5.19 – 1.64, P = 0.269
Increase novelty	Exceed mother	-0.33	-2.49 – 1.57	0.743		\(\beta\) = -0.33, CI = -2.49 – 1.57, P = 0.743
Increase novelty	Male heterogametic	-0.46	-2.87 – 2.16	0.717		\(\beta\) = -0.46, CI = -2.87 – 2.16, P = 0.717
Increase novelty	Exceed upper range	0.25	-1.9 – 2.41	0.833		\(\beta\) = 0.25, CI = -1.9 – 2.41, P = 0.833
Increase novelty	Genetic divergence	0.26	-0.41 – 0.9	0.420		\(\beta\) = 0.26, CI = -0.41 – 0.9, P = 0.42
Increase novelty	Phenotypic divergence	-0.80	-1.48 – -0.23	0.000		\(\beta\) = -0.8, CI = -1.48 – -0.23, P = 0
Increase novelty	Sound traits	0.94	-1.14 – 2.94	0.357		\(\beta\) = 0.94, CI = -1.14 – 2.94, P = 0.357
Increase novelty	Distribution overlap	-1.39	-3.54 – 0.52	0.138		\(\beta\) = -1.39, CI = -3.54 – 0.52, P = 0.138
Increase novelty	Viable reciprocal hybrids	-1.36	-2.97 – 0.04	0.064		\(\beta\) = -1.36, CI = -2.97 – 0.04, P = 0.064
Exceed mother	Male heterogametic	1.68	-0.38 – 3.63	0.104		\(\beta\) = 1.68, CI = -0.38 – 3.63, P = 0.104
Exceed mother	Exceed upper range	-0.84	-1.99 – 0.35	0.156		\(\beta\) = -0.84, CI = -1.99 – 0.35, P = 0.156
Exceed upper range	Genetic divergence	-0.59	-1.19 – 0.02	0.041		\(\beta\) = -0.59, CI = -1.19 – 0.02, P = 0.041
Exceed upper range	Phenotypic divergence	0.08	-0.57 – 0.74	0.790		\(\beta\) = 0.08, CI = -0.57 – 0.74, P = 0.79
Exceed upper range	Male heterogametic	0.69	-1.6 – 2.77	0.532		\(\beta\) = 0.69, CI = -1.6 – 2.77, P = 0.532
Exceed upper range	Sound traits	-1.35	-3.14 – 0.3	0.116		\(\beta\) = -1.35, CI = -3.14 – 0.3, P = 0.116
Exceed upper range	Distribution overlap	-0.30	-1.95 – 1.33	0.715		\(\beta\) = -0.3, CI = -1.95 – 1.33, P = 0.715
Exceed upper range	Viable reciprocal hybrids	2.51	1.01 – 4.01	0.001		\(\beta\) = 2.51, CI = 1.01 – 4.01, P = 0.001

Random effects estimates: alltaxon data

Random effects	Estimates	95% credible interval
Study.ID	1.37	0–6.03
Cross.ID	0.42	0–2.53
spL.name	4.16	0–26.95
SE.units	15.59	0.07–84.7
units	0.74	0.74–0.74

Fixed effects estimates: insect subset data

Group	Factors	Estimates	95% credible interval	P	significance	Description
Increase novelty	Intercept	-0.62	-5.59 – 4.89	0.800		\(\beta\) = -0.62, CI = -5.59 – 4.89, P = 0.8
Increase novelty	Exceed mother	-0.23	-2.57 – 2.11	0.845		\(\beta\) = -0.23, CI = -2.57 – 2.11, P = 0.845
Increase novelty	Male heterogametic	-0.01	-3.11 – 3.38	0.996		\(\beta\) = -0.01, CI = -3.11 – 3.38, P = 0.996
Increase novelty	Exceed upper range	0.79	-1.88 – 3.39	0.560		\(\beta\) = 0.79, CI = -1.88 – 3.39, P = 0.56
Increase novelty	Genetic divergence	0.06	-0.94 – 0.92	0.851		\(\beta\) = 0.06, CI = -0.94 – 0.92, P = 0.851
Increase novelty	Phenotypic divergence	-1.28	-2.23 – -0.3	0.004		\(\beta\) = -1.28, CI = -2.23 – -0.3, P = 0.004
Increase novelty	Sound traits	0.39	-2.55 – 3.3	0.773		\(\beta\) = 0.39, CI = -2.55 – 3.3, P = 0.773
Increase novelty	Distribution overlap	-1.76	-4.63 – 1.01	0.223		\(\beta\) = -1.76, CI = -4.63 – 1.01, P = 0.223
Increase novelty	Viable reciprocal hybrids	-0.90	-3.27 – 1.32	0.435		\(\beta\) = -0.9, CI = -3.27 – 1.32, P = 0.435
Exceed mother	Male heterogametic	0.76	-1.55 – 3.24	0.531		\(\beta\) = 0.76, CI = -1.55 – 3.24, P = 0.531
Exceed mother	Exceed upper range	-0.03	-2.06 – 2.01	0.943		\(\beta\) = -0.03, CI = -2.06 – 2.01, P = 0.943
Exceed upper range	Genetic divergence	-0.77	-1.55 – -0.03	0.042		\(\beta\) = -0.77, CI = -1.55 – -0.03, P = 0.042
Exceed upper range	Phenotypic divergence	-0.47	-1.54 – 0.54	0.369		\(\beta\) = -0.47, CI = -1.54 – 0.54, P = 0.369
Exceed upper range	Male heterogametic	1.88	-0.7 – 4.68	0.160		\(\beta\) = 1.88, CI = -0.7 – 4.68, P = 0.16
Exceed upper range	Sound traits	-0.97	-3.41 – 1.38	0.416		\(\beta\) = -0.97, CI = -3.41 – 1.38, P = 0.416
Exceed upper range	Distribution overlap	-0.89	-3.21 – 1.31	0.433		\(\beta\) = -0.89, CI = -3.21 – 1.31, P = 0.433
Exceed upper range	Viable reciprocal hybrids	0.94	-1.35 – 3.2	0.403		\(\beta\) = 0.94, CI = -1.35 – 3.2, P = 0.403

Random effects estimates: insect data

Random effects	Estimates	95% credible interval
Study.ID	5.97	0–31.8
Cross.ID	2.84	0–16.35
spL.name	30.36	0.18–119.64
SE.units	112.29	0.49–336.88
units	0.74	0.74–0.74

While one of significant moderators (Reciprocal hybrids) become no longer significant in insect subset data, effects are qualitatively the same in sign.

Effects of significant moderators

To visually interpret the impacts of significant moderators, we additionally conducted simpler models for each significant factor. As moderators, we included the focal factors, the compared parental species – spLL or spSS, and the interaction between them. Response variables and random effects of simper models were identical to full models.

# +++++++++++++++++++++++++++++++++++++++++++#
# To set raw metadata in X axis, reload raw metadata and combine to novelty dataset
# +++++++++++++++++++++++++++++++++++++++++++#
rawdat <- dat.full %>%
  select(-Pheno.divergence, -Genet.divergence) %>%
  # lnRR between parental species as phenotypic distance
  mutate(Pheno.divergence = log(Mn.spL/Mn.spS)) %>%
  left_join(
    .,
    read.xlsx("../data/original.data.xlsx", sheet = "Species.level.moderators") %>%
      select(Cross.ID, Genet.divergence)
    ) %>%
  # Natural log of genetic distance
  mutate_at("Genet.divergence", log) %>%
  mutate_at("direction", as.factor)

# +++++++++++++++++++++++++++++++++++++++++++#
# Regression: Genetic distance
# +++++++++++++++++++++++++++++++++++++++++++#

# ## Setting prior to logistic regression ###
# prior <- list(
#   B=list(
#     mu = rep(0, 3), # N of coefficient. (level -1) for categorical factors, 1 for continuous factors, 1 for intercept
#     V = gelman.prior(~ direction:Genet.divergence + Genet.divergence,
#     data = rawdat, # formula and data
#     scale=sqrt(pi^2/3+1))), # error distribution of logistic regression
#   R=list(V=1,fix=1),
#   G = list(
#     G1 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
#     G2 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
#     G3 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
#     G4 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000)
#     )
#   )
# ## Phylogenetic binomial regression ###
# phylmix <-MCMCglmm(
#   fixed = Novelty ~ direction:Genet.divergence + Genet.divergence,
#   # idh(SE):units | weight by SE of effect size
#   random = ~ Study.ID + Cross.ID + spL.name + idh(SE):units,
#   family = "categorical",
#   verbose = FALSE,
#   ginverse = list(spL.name = phylo_MCMC),
#   prior = prior,
#   data = rawdat,
#   nitt = 60000,  # Increase the number of iterations, default is 13000
#   burnin = 5000  # Increase the number of burnin, default is 3000
# )
# phylmix$Sol <- phylmix$Sol/sqrt(1+c2)
# ## Save model ###
# saveRDS(phylmix, file ="../Analysis/Mean.novelty.Genet.divergence.obj")

# +++++++++++++++++++++++++++++++++++++++++++#
# Regression: Phenotypic divergence
# +++++++++++++++++++++++++++++++++++++++++++#
# ## Setting prior to logistic regression ###
# prior <- list(
#   B=list(
#     mu = rep(0, 3), # N of coefficient. (level -1) for categorical factors, 1 for continuous factors, 1 for intercept
#     V = gelman.prior(~ direction:Pheno.divergence + Pheno.divergence,
#     data = rawdat, # formula and data
#     scale=sqrt(pi^2/3+1))), # error distribution of logistic regression
#   R=list(V=1,fix=1),
#   G = list(
#     G1 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
#     G2 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
#     G3 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
#     G4 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000)
#     )
#   )
# ## Phylogenetic binomial regression ###
# phylmix <-MCMCglmm(
#   fixed = Novelty ~ direction:Pheno.divergence + Pheno.divergence,
#   # idh(SE):units | weight by SE of effect size
#   random = ~ Study.ID + Cross.ID + spL.name + idh(SE):units,
#   family = "categorical",
#   verbose = FALSE,
#   ginverse = list(spL.name = phylo_MCMC),
#   prior = prior,
#   data = rawdat,
#   nitt = 60000,  # Increase the number of iterations, default is 13000
#   burnin = 5000  # Increase the number of burnin, default is 3000
# )
# phylmix$Sol <- phylmix$Sol/sqrt(1+c2)
# ## Save model ###
# saveRDS(phylmix, file ="../Analysis/Mean.novelty.Pheno.divergence.obj")

#++++++++++++++++++++++++++++++++++++++++++++++++++#
# Plot
#++++++++++++++++++++++++++++++++++++++++++++++++++#
for (i in c("Genet.divergence", "Pheno.divergence")) {
  
  ## Load model ###
  phylmix <- readRDS(
    paste("../Analysis/Mean.novelty",i, "obj", sep = ".")
    )
  reg <- summary(phylmix$Sol)$statistics %>%
      as.data.frame()
  
  ## Plot ###
  plot <- ggplot(rawdat, aes(x = rawdat[,i], y = rawdat[, "Novelty"])) +
    geom_jitter(
      height = 0.1, width = 0, aes(color = direction), alpha = 0.3, size = 0.2
      ) +
    # novelty for larger mean
    stat_function(
      aes(color = "+"), size = 0.8,
      fun = function(x) 
        1/(1 + exp(-(reg[1,1] + x*(reg[2,1]+reg[3,1]))))
      ) +
    # novelty for smaller mean
    stat_function(
      aes(color = "-"), size = 0.8,
      fun = function(x) 
        1/(1 + exp(-(reg[1,1] + x*reg[2,1])))
      ) +
    ylab("Novelty in phenotypic means") + 
    xlab(paste(i, "(log)")) +
    scale_fill_brewer(palette = "Set2") +
    scale_y_continuous(breaks=seq(0,1)) +
    regressiontheme 
  print(plot)
  # ggsave(
  #   plot = plot + theme(axis.title  = element_blank()),
  #   file = paste("../Analysis/mean.TS.continuous", i, "svg", sep = "."),
  #   height = 1.6, width = 1.7
  #   )

}

for (i in c("Reciprocal", "trait.type")) {

    # Make new categorical data combining direction of TS and target categorical factor
    dat %<>%
      mutate(interaction = str_c(direction, .[, i], sep = "_"))
    
    # ## Setting prior to logistic regression ###
    # prior <- list(
    #   B=list(
    #     mu = rep(0, 4), # N of coefficient. (level -1) for categorical factors, 1 for continuous factors, 1 for intercept
    #     V = gelman.prior(~ interaction, # + trait.direction
    #     data = dat, # formula and data
    #     scale=sqrt(pi^2/3+1))), # error distribution of logistic regression
    #   R=list(V=1,fix=1),
    #   # Replicating same G for the number of random effects (here, 4)
    #   G = list(
    #     G1 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
    #     G2 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
    #     G3 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
    #     G4 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000)
    #     )
    #   )
    # 
    # phylmix <-MCMCglmm(
    #   fixed = Novelty ~  interaction -1, # + trait.direction
    #   family = "categorical",
    #   # idh(SE):units | weight by SE of effect size
    #   random = ~ Study.ID + Cross.ID + spL.name + idh(SE):units,
    #   verbose = FALSE,
    #   ginverse = list(spL.name = phylo_MCMC),
    #   prior = prior,
    #   data = dat
    # )
    # # Correct model result
    # phylmix$Sol <- phylmix$Sol/sqrt(1+c2)
    # ## Save model ###
    # saveRDS(
    #   phylmix,
    #   file =
    #     paste("../Analysis/Mean.novelty", i, "alltaxon.obj", sep = ".")
    #     )

  #++++++++++++++++++++++++++++++++++++++#  
  # ing interaction terms
  #++++++++++++++++++++++++++++++++++++++#
  phylmix <- readRDS(
    paste("../Analysis/Mean.novelty", i, "alltaxon.obj", sep = ".")
    )
  
  sum <- summary(phylmix)$solutions %>%
    as.data.frame() %>%
    rownames_to_column(var = "factors") %>%
    mutate(
      # Impact on exceeding upper or lower range
      direction =
        ifelse(str_detect(factors, "interaction\\+"), 
               "Exceed upper range",
               "Exceed lower range"
               ) %>%
        as.factor
      ) %>%
    within(direction <- ordered(direction, c("Exceed upper range", "Exceed lower range"))) %>%
    ## Change factor names ###
    within(
      factors <- str_remove_all(factors, "interaction._")
      )       
  
    #++++++++++++++++++++++++++++++++++++++#  
    # Plot
    #++++++++++++++++++++++++++++++++++++++#
    categoricalplot <- ggplot(sum, aes(x = post.mean, y = factors)) +
      # Vertical line
      geom_vline(
        xintercept = 0, size = 0.3,
        colour = "grey30", linetype = "dotted"
        ) +
      # CI
      geom_errorbarh(
        aes(xmin = sum[, 'l-95% CI'], xmax = sum[, 'u-95% CI']),
        height = .0001
        ) +
      geom_point(size = 1.3, shape = 17) +
      # Title
      ylab("") + xlab("Effect on novelty in phenotypic means") +
      ggtitle(i) +
      # Combine different plots for main factors and interactions
      facet_grid(
        direction ~., scales = "free", drop = TRUE,
        labeller = label_wrap_gen(width = 11)
        ) +
      foresttheme
    print(categoricalplot)
  
    # ggsave(
    #     plot = categoricalplot + theme(axis.text.y = element_blank()),
    #     file = paste("../Analysis/Mean.TS.categorical", i,taxon, "svg", sep = "."),
    #     height = 1.8, width = 2.5
    #     )

}

Novelty in phenotypic variabilities (using CV)

Frequency

dat.full <- read.csv("../data/variation.ES.Novelty.csv", head = TRUE)

## N of TS in each taxa and each direction ###
summary <- bind_rows(
  # All observations
  dat.full %>%
    group_by(taxa) %>%
    summarise(
      'Species pair' = length(unique(species.pair)),
      Observations = length(unique(ES.ID)),
      Study = length(unique(Study.ID))
      ) %>%
    mutate(direction = "All Observations")
  ,
  # TS for any direction
  dat.full %>%
    filter(Novelty == "1") %>%
    group_by(taxa) %>%
    summarise(
      'Species pair' = length(unique(species.pair)),
      Observations = length(unique(ES.ID)),
      Study = length(unique(Study.ID))
      ) %>%
    mutate(direction = "Increase novelty")
  ,
  # TS for each direction
  dat.full %>%
    filter(Novelty == "1") %>%
    group_by(taxa, direction) %>%
    summarise(
      'Species pair' = length(unique(species.pair)),
      Observations = length(unique(ES.ID)),
      Study = length(unique(Study.ID))
      )
  ) %>%
  # Assign 0 for no observation of TS
  replace(., is.na(.), "0") %>% 
  # Make tidy data
  gather(key = metrics, value = N, -c(taxa, direction)) %>%
  # Order factors
  mutate_at(vars("metrics", "direction"), as.factor) %>%
  filter(metrics != "Study")

# Order taxon
summary$metrics <- ordered(summary$metrics, levels = c("Study", "Species pair", "Observations"))
# Order TS category
summary$direction <- ordered(summary$direction, levels = c("All Observations", "Increase novelty", "+", "-"))
# Change names of TS category to more intuitive name
levels(summary$direction) <- c("All Observations", "Increase novelty", "Exceed upper range", "Exceed lower range")

## Plot ###
plot <- ggplot(
  # rename levels of factors to wrap label text
  summary %>%
    within(levels(direction) <- c("All Obser- vations", "Novel phenotypic variabilities", "Exceed upper range", "Exceed lower range")) %>%
    within(levels(metrics) <- c("Study", "Species pair", "Obser- vations")), 
  aes(x = "", y = N, fill = taxa)) +
  geom_bar(width = 1, stat = "identity") +
  scale_fill_brewer(palette = "Paired") +
  facet_grid(
    scale = "free", metrics ~ direction,
    # strip text into two lines
    labeller = label_wrap_gen(width = 10)
    ) +
  xlab("") + ylab("") +
  stackedbartheme
plot +
  ggtitle("Taxonomic distribution of novel phenotypic variabilities")

# ggsave(
#   plot = plot,
#   file = "../Analysis/Variation.TS.frequency.svg", height = 3.7, width = 5.0
#   )

## Total number of observations and percentage ###
summary %>%
  group_by(direction, metrics) %>%
  # Count number of observaitons and species pairs
  summarise(sum(N)) %>%
  spread(key = metrics, value = "sum(N)") %>%
  as.data.frame() %>%
  # Calculate percentage
  mutate(
    'Species pair %' = .[, "Species pair"]/.[1, "Species pair"]*100,
    'Observaton %' = Observations/.[1, "Observations"]*100
  ) %>%
  mutate_at(vars(contains("percent")), round, 2) %>%
  kable("html", digits = 3, caption = "Summary of the novel phenotypic variabilities frequency") %>% 
  kable_styling("striped", position = "left")

Summary of the novel phenotypic variabilities frequency

direction	Species pair	Observations	Species pair %	Observaton %
All Observations	33	331	100.000	100.000
Increase novelty	31	248	93.939	74.924
Exceed upper range	18	82	54.545	24.773
Exceed lower range	26	170	78.788	51.360

Factors affecting novelty in phenotypic variability

We checked robustness of results by conducting identical analysis for insect subset data (removing vertebrate data).

Model, which was ran using MCMCglmm function, included following Random effects estimates:

• Study.ID: Primary studies

• Cross.ID: Parental strain used in the crossing. Discriminate intraspecific populations

• spL.name: Phylogeny of parental species (spLL_V)

• SE.units: Sampling variance of effect size

The moderators were categorised into 3 subsets: main effects (labelled as novelty in phenotypic variabilities), interaction terms with compared parental species (spLL vs. spSS, labelled as exceed upper range; mother species vs. father, labelled as exceed mother).

#++++++++++++++++++++++++++++++++++#
# Phylogenetic tree for mcmcglmm 
#++++++++++++++++++++++++++++++++++#

# compute branch lengths of tree
phylo_branch <- read.tree(file = "../data/phylo.variation.tre") %>%
  compute.brlen(method = "Grafen", power = 1)

# saving phylogeneic matrix
phylo_cor <- vcv(phylo_branch, cor = T)

# generating inverse phylogenetic matrix for MCMCglmm
phylo_branch$node.label <- NULL
phylo_MCMC <- MCMCglmm::inverseA(phylo_branch, nodes = "ALL", scale = TRUE)$Ainv


#+++++++++++++++++++++++++++++++++++++#
# Phylogenetic comparative analysis
#+++++++++++++++++++++++++++++++++++++#

# Caption
cap <- data.frame(
  desc = c(
    "Table S8. Fixed effects estimates of Bayesian logistic model investigating factors affecting novelty in phenotypic variabilities. Data of all taxon was used",
    "Fixed effects estimates: insect subset data"
    )
  ) %>%
  mutate_all(as.character)
row.names(cap) <- c("alltaxon", "insect")

for (taxon in c("alltaxon", "insect")) {
  
  if (taxon == "alltaxon") {
    dat <- dat.full
  } else {
    dat <- dat.full %>% 
      filter(insect == taxon)
  }
  
  # ## Setting prior to logistic regression ###
  # prior <- list(
  #   B=list(
  #     mu = rep(0, 17), # N of coefficient. (level -1) for categorical factors, 1 for continuous factors, 1 for intercept
  #     V = gelman.prior(~
  #     parental*Hetero.sex + parental*direction +
  #     direction*Genet.divergence + direction*Pheno.divergence +
  #     direction*Hetero.sex + direction*trait.type +
  #     direction*Distribution + direction*Reciprocal,
  #     data = dat, # formula and data
  #     scale=sqrt(pi^2/3+1))), # error distribution of logistic regression
  #   R=list(V=1,fix=1),
  #   # Replicating same G for the number of random effects (here, 4)
  #   G = list(
  #     G1 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
  #     G2 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
  #     G3 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
  #     G4 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000)
  #     )
  #   )
  # ## Phylogenetic binomial regression ###
  # phylmix <-MCMCglmm(
  #   fixed = Novelty ~
  #     parental*Hetero.sex + parental*direction +
  #     direction*Genet.divergence + direction*Pheno.divergence +
  #     direction*Hetero.sex + direction*trait.type +
  #     direction*Distribution + direction*Reciprocal,
  #   family = "categorical",
  #   # idh(SE):units | weight by SE of effect size
  #   random = ~ Study.ID + Cross.ID + spL.name + idh(SE):units,
  #   verbose = FALSE,
  #   ginverse = list(spL.name = phylo_MCMC),
  #   prior = prior,
  #   data = dat,
  #   nitt = 60000,  # Increase the number of iterations, default is 13000
  #   burnin = 5000  # Increase the number of burnin, default is 3000
  # )
  # ## Correcting estimate ###
  # phylmix$Sol <- phylmix$Sol/sqrt(1+c2)
  # ## Save model ###
  # saveRDS(
  #   phylmix,
  #   file = paste("../Analysis/Variation.novelty.allfactors", taxon, "obj", sep = ".")
  #   )
  
  ## Load model ###
  phylmix <- readRDS(file = paste("../Analysis/Variation.novelty.allfactors", taxon, "obj", sep = "."))
  
  sum <- get_fixed.MCMCglmm(summary(phylmix)$solutions) %>%
    ## ing Factors ###
    mutate(
      Group = 
        ifelse(str_detect(Factors, "direction.:"), 
               "Exceed upper range",
               ifelse(str_detect(Factors, "parentalMother:"),
                      "Exceed mother",
                      ifelse(Factors == "Hetero.sexMale:direction+",
                             "Exceed upper range",
                             "Increase novelty"
                             )
                      )
               )
    ) %>%
    mutate_at("Group", as.factor) %>%
    within(Group <- ordered(Group, c("Increase novelty", "Exceed upper range", "Exceed mother"))) %>%
    ## Change factor names ###
    within(
      Factors <- str_remove_all(
        Factors, "direction.:"
        ) %>%
        str_remove_all(., "parentalMother:") %>%
        str_remove_all(., ":direction.")
      ) %>%
    within(
      Factors <- factor(
        Factors, ordered = TRUE, 
        levels = c("parentalMother", "direction+", "trait.typesound", "Hetero.sexMale", "ReciprocalViable", "DistributionOverlap", "Pheno.divergence",  "Genet.divergence",  "(Intercept)")
        )
      )
  levels(sum$Factors) <- c("Exceed mother", "Exceed upper range", "Sound traits", "Male heterogametic", "Viable reciprocal hybrids", "Distribution overlap", "Phenotypic divergence", "Genetic divergence", "Intercept")
  
  ## Plot ###
  metaplot <- ggplot(sum, aes(x = post.mean, y = Factors)) +
    # Vertical line
    geom_vline(
      xintercept = 0, size = 0.2, 
      colour = "grey30", linetype = "dotted"         
      ) +
    # CI
    geom_errorbarh(
      aes(
        xmin = sum[, 'l-95% CI'], xmax = sum[, 'u-95% CI'],
        colour=significance 
        ), 
      height = .0001
      ) +
    # Color of plots and errorbars
    scale_colour_manual(values = c("grey60", "black")) +
    geom_point(size = 1, aes(colour = significance)) +
    scale_fill_manual(values = c("grey60", "black")) +
    # Title
    ggtitle(paste("Fixed effects estimates using", taxon, "data")) +
    ylab("") + xlab("Estimate with 95% CI") +
    # Combine different plots for main Factors and interactions
    facet_grid(
      Group~., scales = "free", space = "free",
      labeller = label_wrap_gen(width = 20)
      ) +
    # Themes
    foresttheme
  print(metaplot)
  
  # ggsave(
  #     plot = metaplot, 
  #     file = paste("../Analysis/variation.TS.result", taxon, "svg", sep = "."), 
  #     height = 4.7, width = 4.0
  #     )
  
  #+++++++++++++++++++++++++#
  # Fixed effects output
  #+++++++++++++++++++++++++#
  sum %>% 
    select(Group, Factors, Estimates, '95% credible interval', P, significance, Description) %>%
    kable("html", caption = cap[taxon,]) %>% 
    kable_styling("striped", position = "left") %>%
    scroll_box(width = "100%", height = "500px") %>%
    print()
  
  #+++++++++++++++++++++++++++#
  # Random effects output 
  #+++++++++++++++++++++++++++#
  # transformation for varaince
  get_random.MCMCglmm(summary(phylmix$VCV/(1+c2))) %>%
    kable("html", digits = 2,
          caption = paste("Random effects estimates: ", taxon, "data", " ")
          ) %>% 
    kable_styling("striped", position = "left") %>%
    print()
  
}

Table S8. Fixed effects estimates of Bayesian logistic model investigating factors affecting novelty in phenotypic variabilities. Data of all taxon was used

Group	Factors	Estimates	95% credible interval	P	significance	Description
Increase novelty	Intercept	-0.41	-3.57 – 3.2	0.769		\(\beta\) = -0.41, CI = -3.57 – 3.2, P = 0.769
Increase novelty	Exceed mother	0.54	-1.43 – 2.43	0.588		\(\beta\) = 0.54, CI = -1.43 – 2.43, P = 0.588
Increase novelty	Male heterogametic	-0.06	-2.68 – 2.33	0.961		\(\beta\) = -0.06, CI = -2.68 – 2.33, P = 0.961
Increase novelty	Exceed upper range	-1.36	-3.52 – 0.78	0.210		\(\beta\) = -1.36, CI = -3.52 – 0.78, P = 0.21
Increase novelty	Genetic divergence	-0.47	-1.23 – 0.26	0.220		\(\beta\) = -0.47, CI = -1.23 – 0.26, P = 0.22
Increase novelty	Phenotypic divergence	-0.39	-1.02 – 0.2	0.208		\(\beta\) = -0.39, CI = -1.02 – 0.2, P = 0.208
Increase novelty	Sound traits	1.68	-0.31 – 3.83	0.109		\(\beta\) = 1.68, CI = -0.31 – 3.83, P = 0.109
Increase novelty	Distribution overlap	-0.69	-2.55 – 1.26	0.464		\(\beta\) = -0.69, CI = -2.55 – 1.26, P = 0.464
Increase novelty	Viable reciprocal hybrids	-1.07	-2.72 – 0.62	0.203		\(\beta\) = -1.07, CI = -2.72 – 0.62, P = 0.203
Exceed mother	Male heterogametic	-1.44	-3.45 – 0.43	0.142		\(\beta\) = -1.44, CI = -3.45 – 0.43, P = 0.142
Exceed mother	Exceed upper range	-0.22	-1.7 – 1.33	0.790		\(\beta\) = -0.22, CI = -1.7 – 1.33, P = 0.79
Exceed upper range	Genetic divergence	1.25	0.47 – 2	0.000		\(\beta\) = 1.25, CI = 0.47 – 2, P = 0
Exceed upper range	Phenotypic divergence	0.83	0.01 – 1.61	0.036		\(\beta\) = 0.83, CI = 0.01 – 1.61, P = 0.036
Exceed upper range	Male heterogametic	0.47	-1.84 – 2.7	0.678		\(\beta\) = 0.47, CI = -1.84 – 2.7, P = 0.678
Exceed upper range	Sound traits	-3.29	-5.21 – -1.38	0.001		\(\beta\) = -3.29, CI = -5.21 – -1.38, P = 0.001
Exceed upper range	Distribution overlap	1.32	-0.53 – 3.08	0.151		\(\beta\) = 1.32, CI = -0.53 – 3.08, P = 0.151
Exceed upper range	Viable reciprocal hybrids	1.19	-0.55 – 2.98	0.177		\(\beta\) = 1.19, CI = -0.55 – 2.98, P = 0.177

Random effects estimates: alltaxon data

Random effects	Estimates	95% credible interval
Study.ID	2.07	0.02–7.41
Cross.ID	0.53	0–2.78
spL.name	2.54	0–13.9
SE.units	92.08	37.22–172.67
units	0.74	0.74–0.74

Fixed effects estimates: insect subset data

Group	Factors	Estimates	95% credible interval	P	significance	Description
Increase novelty	Intercept	-0.22	-5.65 – 5.45	0.912		\(\beta\) = -0.22, CI = -5.65 – 5.45, P = 0.912
Increase novelty	Exceed mother	0.62	-1.83 – 3	0.627		\(\beta\) = 0.62, CI = -1.83 – 3, P = 0.627
Increase novelty	Male heterogametic	0.04	-3.13 – 3.38	0.965		\(\beta\) = 0.04, CI = -3.13 – 3.38, P = 0.965
Increase novelty	Exceed upper range	-1.14	-3.82 – 1.56	0.403		\(\beta\) = -1.14, CI = -3.82 – 1.56, P = 0.403
Increase novelty	Genetic divergence	-0.14	-1.09 – 0.93	0.777		\(\beta\) = -0.14, CI = -1.09 – 0.93, P = 0.777
Increase novelty	Phenotypic divergence	-0.41	-1.39 – 0.57	0.408		\(\beta\) = -0.41, CI = -1.39 – 0.57, P = 0.408
Increase novelty	Sound traits	0.78	-2.25 – 3.69	0.586		\(\beta\) = 0.78, CI = -2.25 – 3.69, P = 0.586
Increase novelty	Distribution overlap	-0.57	-3.54 – 2.24	0.695		\(\beta\) = -0.57, CI = -3.54 – 2.24, P = 0.695
Increase novelty	Viable reciprocal hybrids	-0.78	-3.2 – 1.82	0.527		\(\beta\) = -0.78, CI = -3.2 – 1.82, P = 0.527
Exceed mother	Male heterogametic	-0.55	-3.03 – 1.94	0.677		\(\beta\) = -0.55, CI = -3.03 – 1.94, P = 0.677
Exceed mother	Exceed upper range	0.25	-2.25 – 2.62	0.842		\(\beta\) = 0.25, CI = -2.25 – 2.62, P = 0.842
Exceed upper range	Genetic divergence	0.83	-0.1 – 1.78	0.085		\(\beta\) = 0.83, CI = -0.1 – 1.78, P = 0.085
Exceed upper range	Phenotypic divergence	1.04	-0.1 – 2.15	0.067		\(\beta\) = 1.04, CI = -0.1 – 2.15, P = 0.067
Exceed upper range	Male heterogametic	-0.21	-3.07 – 2.59	0.896		\(\beta\) = -0.21, CI = -3.07 – 2.59, P = 0.896
Exceed upper range	Sound traits	-2.45	-4.95 – 0.41	0.079		\(\beta\) = -2.45, CI = -4.95 – 0.41, P = 0.079
Exceed upper range	Distribution overlap	0.79	-1.78 – 3.36	0.544		\(\beta\) = 0.79, CI = -1.78 – 3.36, P = 0.544
Exceed upper range	Viable reciprocal hybrids	0.03	-2.76 – 2.69	0.975		\(\beta\) = 0.03, CI = -2.76 – 2.69, P = 0.975

Random effects estimates: insect data

Random effects	Estimates	95% credible interval
Study.ID	13.88	0.36–51.78
Cross.ID	1.68	0–8.89
spL.name	18.74	0.08–74.21
SE.units	565.34	123.22–1224.95
units	0.74	0.74–0.74

While significant moderators become no longer significant in insect subset data, effects are qualitatively the same in sign.

Effects of significant moderators

To visually interpret the impacts of significant moderators, we additionally conducted simpler models for each significant factor. As moderators, we included the focal factors, the compared parental species – spLL or spSS, and the interaction between them. Response variables and random effects of simper models were identical to full models.

# +++++++++++++++++++++++++++++++++++++++++++#
# To set raw metadata in X axis, reload raw metadata and combine to transgression dataset
# +++++++++++++++++++++++++++++++++++++++++++#
rawdat <- dat.full %>%
  select(-Pheno.divergence, -Genet.divergence) %>%
  # lnRR between parental species as phenotypic distance
  mutate(Pheno.divergence = abs(log(Mn.spL/Mn.spS))) %>%
  left_join(
    .,
    read.xlsx("../data/original.data.xlsx", sheet = "Species.level.moderators") %>%
      select(Cross.ID, Genet.divergence)
    ) %>%
  # Natural log of genetic distance
  mutate_at("Genet.divergence", log)

# # +++++++++++++++++++++++++++++++++++++++++++#
# # Regression: Genetic distance
# # +++++++++++++++++++++++++++++++++++++++++++#
# 
# ## Setting prior to logistic regression ###
# prior <- list(
#   B=list(
#     mu = rep(0, 3), # N of coefficient. (level -1) for categorical factors, 1 for continuous factors, 1 for intercept
#     V = gelman.prior(~ direction:Genet.divergence + Genet.divergence,
#     data = rawdat, # formula and data
#     scale=sqrt(pi^2/3+1))), # error distribution of logistic regression
#   R=list(V=1,fix=1),
#   G = list(
#     G1 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
#     G2 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
#     G3 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
#     G4 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000)
#     )
#   )
# ## Phylogenetic binomial regression ###
# phylmix <-MCMCglmm(
#   fixed = Novelty ~ direction:Genet.divergence + Genet.divergence,
#   # idh(SE):units | weight by SE of effect size
#   random = ~ Study.ID + Cross.ID + spL.name + idh(SE):units,
#   family = "categorical",
#   verbose = FALSE,
#   ginverse = list(spL.name = phylo_MCMC),
#   prior = prior,
#   data = rawdat,
#   nitt = 60000,  # Increase the number of iterations, default is 13000
#   burnin = 5000  # Increase the number of burnin, default is 3000
# )
# phylmix$Sol <- phylmix$Sol/sqrt(1+c2)
# ## Save model ###
# saveRDS(phylmix, file ="../Analysis/Variation.novelty.Genet.divergence.obj")
# 
# # +++++++++++++++++++++++++++++++++++++++++++#
# # Regression: Phenotypic divergence
# # +++++++++++++++++++++++++++++++++++++++++++#
# 
# ## Setting prior to logistic regression ###
# prior <- list(
#   B=list(
#     mu = rep(0, 3), # N of coefficient. (level -1) for categorical factors, 1 for continuous factors, 1 for intercept
#     V = gelman.prior(~ direction:Pheno.divergence + Pheno.divergence,
#     data = rawdat, # formula and data
#     scale=sqrt(pi^2/3+1))), # error distribution of logistic regression
#   R=list(V=1,fix=1),
#   G = list(
#     G1 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
#     G2 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
#     G3 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
#     G4 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000)
#     )
#   )
# ## Phylogenetic binomial regression ###
# phylmix <-MCMCglmm(
#   fixed = Novelty ~ direction:Pheno.divergence + Pheno.divergence,
#   # idh(SE):units | weight by SE of effect size
#   random = ~ Study.ID + Cross.ID + spL.name + idh(SE):units,
#   family = "categorical",
#   verbose = FALSE,
#   ginverse = list(spL.name = phylo_MCMC),
#   prior = prior,
#   data = rawdat,
#   nitt = 60000,  # Increase the number of iterations, default is 13000
#   burnin = 5000  # Increase the number of burnin, default is 3000
# )
# phylmix$Sol <- phylmix$Sol/sqrt(1+c2)
# ## Save model ###
# saveRDS(phylmix, file ="../Analysis/Variation.novelty.Pheno.divergence.obj")

#++++++++++++++++++++++++++++++++++++++++++++++++++#
# Plot
#++++++++++++++++++++++++++++++++++++++++++++++++++#

for (i in c("Genet.divergence", "Pheno.divergence")) {
  
  ## Load model ###
  phylmix <- readRDS(
    paste("../Analysis/Variation.novelty",i, "obj", sep = ".")
    )
  reg <- summary(phylmix$Sol)$statistics %>%
      as.data.frame()
  
  ## Plot ###
  plot <- ggplot(rawdat, aes(x = rawdat[,i], y = rawdat[, "Novelty"])) +
    geom_jitter(
      height = 0.1, width = 0, aes(color = direction), alpha = 0.3, size = 0.2
      ) +
    # Larger variability
    stat_function(
      aes(color = "+"), size = 0.8,
      fun = function(x) 
        1/(1 + exp(-(reg[1,1] + x*(reg[2,1]+reg[3,1]))))
      ) +
    # Smaller variability
    stat_function(
      aes(color = "-"), size = 0.8,
      fun = function(x) 
        1/(1 + exp(-(reg[1,1] + x*reg[2,1])))
      ) +
    ylab("Novel phenotypic variabilities") + 
    xlab(paste(i, "(log)")) +
    scale_fill_brewer(palette = "Set2") +
    scale_y_continuous(breaks=seq(0,1)) +
    regressiontheme 
  
  print(plot)
  # ggsave(
  #   plot = plot, 
  #   file = paste("../Analysis/Variation.TS.continuous", i, "svg", sep = "."), 
  #   height = 1.6, width = 1.7
  #   )

}

for (i in c("Reciprocal", "trait.type")) {

  # Make new categorical data combining direction of TS and target categorical factor
  dat %<>%
    mutate(interaction = str_c(direction, .[, i], sep = "_"))

  # ## Setting prior to logistic regression ###
  # prior <- list(
  #   B=list(
  #     mu = rep(0, 4), # N of coefficient. (level -1) for categorical factors, 1 for continuous factors, 1 for intercept
  #     V = gelman.prior(~ interaction,
  #     data = dat, # formula and data
  #     scale=sqrt(pi^2/3+1))), # error distribution of logistic regression
  #   R=list(V=1,fix=1),
  #   # Replicating same G for the number of random effects (here, 4)
  #   G = list(
  #     G1 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
  #     G2 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
  #     G3 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
  #     G4 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000)
  #     )
  #   )
  # ## Run model ###
  # phylmix <-MCMCglmm(
  #   fixed = Novelty ~  interaction -1, # + trait.direction
  #   family = "categorical",
  #   # idh(SE):units | weight by SE of effect size
  #   random = ~ Study.ID + Cross.ID + spL.name + idh(SE):units,
  #   verbose = FALSE,
  #   ginverse = list(spL.name = phylo_MCMC),
  #   prior = prior,
  #   data = dat
  # )
  # # Correcting estimate
  # phylmix$Sol <- phylmix$Sol/sqrt(1+c2)
  # ## Save model ###
  # saveRDS(
  #   phylmix,
  #   file =
  #     paste("../Analysis/Variation.novelty", i, "alltaxon.obj", sep = ".")
  #     )
  
  #++++++++++++++++++++++++++++++++++++++#  
  # Grouping interaction terms
  #++++++++++++++++++++++++++++++++++++++#
  phylmix <- readRDS(
    paste("../Analysis/Variation.novelty", i, "alltaxon.obj", sep = ".")
    )
  
  sum <- summary(phylmix)$solutions %>%
    as.data.frame() %>%
    rownames_to_column(var = "factors") %>%
    mutate(
      # Impact on exceeding upper or lower range
      direction =
        ifelse(str_detect(factors, "interaction\\+"), 
               "Exceed upper range",
               "Exceed lower range"
               ) %>%
        as.factor
      ) %>%
    within(direction <- ordered(direction, c("Exceed upper range", "Exceed lower range"))) %>%
    ## Change factor names ###
    within(
      factors <- str_remove_all(factors, "interaction._")
      )       
  
    #++++++++++++++++++++++++++++++++++++++#  
    # Plot
    #++++++++++++++++++++++++++++++++++++++#
    categoricalplot <- ggplot(sum, aes(x = post.mean, y = factors)) +
      # Vertical line
      geom_vline(
        xintercept = 0, size = 0.3,
        colour = "grey30", linetype = "dotted"
        ) +
      # CI
      geom_errorbarh(
        aes(xmin = sum[, 'l-95% CI'], xmax = sum[, 'u-95% CI']),
        height = .0001
        ) +
      geom_point(size = 1.3, shape = 17) +
      # Title
      ylab("") + xlab("Effect on novelty in phenotypic means") +
      ggtitle(i) +
      # Combine different plots for main factors and interactions
      facet_grid(
        direction ~., scales = "free", drop = TRUE,
        labeller = label_wrap_gen(width = 11)
        ) +
      foresttheme
    print(categoricalplot)
  
  # ggsave(
  #     plot = categoricalplot + theme(axis.text.y = element_blank()),
  #     file = 
  #       paste("../Analysis/Variation.novelty.categorical", i,taxon, "svg", sep = "."),
  #     height = 1.8, width = 2.5
  #     )
  
}

Phenotypic variability of novel phenotype

Dataset summary

Relative variability indicates F1 hybrids’ phenotypic variability size (CV) compared to those of parents.

• Smaller: smaller variability than of both parents

• Intermediate: intermediate between parents’ variability

• Larger: larger variability than of both parents

dat <- read.csv("../data/dat.novelty.csv", head = TRUE) %>% 
  mutate_at(vars("mean.novelty", "relative.var"), as.factor) %>% 
  within(
    relative.var <-
      ordered(relative.var, levels = c("Smaller", "Intermediate", "Larger")),
    mean.novelty <- 
      ordered(mean.novelty, levels = c("Nonnovel", "Novel"))
  )

dat.relat.var <- dat %>% 
  group_by(mean.novelty, relative.var) %>% 
  summarise('N (Trait observation)' = length(ES.ID)) %>% 
  spread(key = mean.novelty, value = 'N (Trait observation)') %>%
  as.data.frame() %>%
  mutate(
    "Nonnovel phenotype (% trait obs)" = 
      Nonnovel/sum(Nonnovel)*100, 
    "Novel phenotype (% trait obs)" = 
      Novel/sum(Novel)*100,
    Trait.count = Nonnovel + Novel # N of traits at each variability levels
  ) %>%
  rename('Relative.variability' = 'relative.var')

ggplot(
  dat.relat.var %>%
    dplyr::select(-contains("trait obs"), -Trait.count) %>%
    pivot_longer(
      col = -Relative.variability,
      names_to = "Phenotypic.novelty",
      values_to = "Count"
      ) %>%
    left_join(dat.relat.var) %>%
    as.data.frame %>%
    mutate_at("Phenotypic.novelty", as.factor),
  aes(
    y = Count, 
    x = Trait.count/2,  # ensure each pie chart is "left-aligned"
    width = Trait.count, # sample size (N of traits at each variability levels)
    fill = Phenotypic.novelty
    )
  ) +
  geom_bar(position="fill", stat = "identity") +
  facet_grid(~ Relative.variability) +
  # Pie chart
  coord_polar("y") +
  ggtitle("Novel and non-novel phenotypes occupancies at
          each relative variability levels") +
  xlab("Trait counts") + ylab("Relative variability levels") +
  theme(axis.text.x = element_blank())

dat.relat.var %>%
  rename(
  'Nonnovel.phenotype (N)' = 'Nonnovel',
  'Novel.phenotype (N)' = 'Novel'
  ) %>%
  kable("html", digits = 1, caption = "Dataset summary") %>%
  kable_styling("striped", position = "left")

Dataset summary

Relative.variability	Nonnovel.phenotype (N)	Novel.phenotype (N)	Nonnovel phenotype (% trait obs)	Novel phenotype (% trait obs)	Trait.count
Smaller	146	74	49.2	40.4	220
Intermediate	96	54	32.3	29.5	150
Larger	55	55	18.5	30.1	110

#++++++++++++++++++++++++++++++++++++++++++++++++++++++#
# Read create tree based on Open Tree of Life taxonomy
#++++++++++++++++++++++++++++++++++++++++++++++++++++++#

# # matching names from open tree taxonomy
# taxa <- tnrs_match_names(
#   names = levels(dat$sp1.name) %>%
#     str_replace_all("_", " "),
#   context_name = "Animals"
#   )
# 
# # Create tree
# tree <- tol_induced_subtree(ott_ids = taxa$ott_id)
# tree$tip.label %<>%
#   strip_ott_ids # remove OTT IDs from tip labels
# # randomly solve non-binary phylogeny
# set.seed(6)
# bin.tree <- multi2di(tree, random = T)
# print("Randomly solved phylogeny")
# 
# # Indicate mismatch between tip labels & dataset species names
# setdiff(levels(as.factor(bin.tree$tip.label)), levels(dat$sp1.name))
# setdiff(levels(dat$sp1.name), levels(as.factor(bin.tree$tip.label)))
# # Fix names of tip labels
# bin.tree$tip.label %<>% str_replace_all("Dryophytes", "Hyla")
# 
# write.tree(bin.tree, file= "../data/phylo.novelty.tre")

#++++++++++++++++++++++++++++++++++#
# Phylogenetic tree for mcmcglmm 
#++++++++++++++++++++++++++++++++++#

# compute branch lengths of tree
phylo_branch <- read.tree(file = "../data/phylo.novelty.tre") %>%
  compute.brlen(method = "Grafen", power = 1)
plot.phylo(phylo_branch, cex = 0.7)

Figure S3. Phylogenetic tree used, which was based on tree of the parental species within a cross which name comes earlier in alphabetical order

# saving phylogeneic matrix
phylo_cor <- vcv(phylo_branch, cor = T)

# generating inverse phylogenetic matrix for MCMCglmm
phylo_branch$node.label <- NULL
phylo_MCMC <- MCMCglmm::inverseA(phylo_branch, nodes = "ALL", scale = TRUE)$Ainv

Phylogenetic ordinal (probit) regression

Here we ask if phenotypic novelty associate with relative variability levels. We conducted ordinal regression, employing categorical ordered variability levels (smaller < intermediate < larger, see above) as response variable, and categorical trait mean novelty levels (fixed effect mean.novelty with 2 levels: Novel vs Nonnovel) as moderator variable, using MCMCglmm function. The model included following Random effects estimates:

• Study.ID: Primary studies

• sp1.name: Phylogeny of parental species

• Cross.ID: Parental strain used in the crossing. Discriminate intraspecific populations

• cross: Crossing direction

• SE.units: Sampling variance of effect size

# prior1<-list(
#   B=list(
#     mu=c(0,0),
#     V=gelman.prior(~mean.novelty, data=select(dat, -trait), scale=sqrt(1+1))
#     ),
#   R=list(V=1,fix=1),
#   G = list(
#     G1 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
#     G2 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
#     G3 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
#     G3 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
#     G4 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000)
#     )
#   )
# 
# phylmix <-MCMCglmm(
#   fixed = relative.var ~ mean.novelty,
#   family = "ordinal",
#   random = ~ Study.ID + Cross.ID + sp1.name + cross + ES.ID,
#   verbose = FALSE,
#   prior = prior1,
#   ginverse = list(sp1.name = phylo_MCMC),
#   data = select(dat, -trait),
#   nitt = 60000,  # Increase the number of iterations, default is 13000
#   burnin = 5000  # Increase the number of burnin, default is 3000
#   )
# 
# ## Save model ###
# saveRDS(phylmix, file = "../Analysis/novelty.mean.var.obj")

## Load model ###
phylmix <- readRDS("../Analysis/novelty.mean.var.obj")

#+++++++++++++++++++++++++++#
# Fixed effects output 
#+++++++++++++++++++++++++++#
get_fixed.MCMCglmm(summary(phylmix)$solutions) %>%
  dplyr::select("Factors", "Estimates", "95% credible interval", "P", "Description") %>%
  kable("html", digits = 3, caption = "Fixed effects estimates") %>% 
  kable_styling("striped", position = "left")

Fixed effects estimates

Factors	Estimates	95% credible interval	P	Description
(Intercept)	0.23	-1.72 – 1.94	0.678	\(\beta\) = 0.23, CI = -1.72 – 1.94, P = 0.678
mean.noveltyNovel	1.01	0.45 – 1.62	0.000	\(\beta\) = 1.01, CI = 0.45 – 1.62, P = 0

#+++++++++++++++++++++++++++#
# Random effects output 
#+++++++++++++++++++++++++++#
# transformation for varaince
get_random.MCMCglmm(summary(phylmix$VCV/(1+c2))) %>%
  kable("html", digits = 2, caption = "Random effects estimates") %>% 
  kable_styling("striped", position = "left")

Random effects estimates

Random effects	Estimates	95% credible interval
Study.ID	1.27	0.14–3.61
Cross.ID	0.23	0–1.12
sp1.name	0.52	0–3.04
cross	18.92	0–138.18
ES.ID	3.21	1.34–5.84
units	0.74	0.74–0.74

The phenotypic novelty (mean.noveltyNovel) influenced positively. This indicates that, compared to non-novel phenotypes, novel phenotypes have greater chances to be more variable than parents’ phenotypes.

Trait mosaicism

F1 hybrids can be seen as “a mosaic of both parental and intermediate morphological characters rather than just intermediate ones” Rieseberg and Ellstrand 1993. That is, F1 hybrid individuals often consist of trats closely resemble one parent, intermediate, and resemble the other parent. Mosaicism indicates tr integration of parents are often collapse in F1 hybrids. Here we investigate the strength of trait mosaicism in male mating traits.

d_mismatch calculation

To quantify trait mosaicism in each crossing, we calculated d_mismatch following Thompson et al. (2021). d_mismatch was calculated as follows:

\(d_{mismatch} = \sqrt{2} * \sqrt{ Z_i^2 + Z_j^2 - \frac{Z_i + Z_j}{\sqrt{2}}^2 }\)
Where \(Z_i\), standardized hybrid trait value in trait \(i\), is
\(Z_i = \frac{hyb_i - spSS_i}{spLL_i - spSS_i}\)

\(Z_i\) is 0-1 if F1 phenotypic mean lies within the range of parents. We note, however, novelty in phenotypic means (transgressive segregation) can result in quite large \(Z_i\): when trait size is large and parental phenotypic divergence is small, novel hybrid phenotype will result in quite large \(Z_i\). This seems occurred in mice (see bellow)

dat <- original.dat %>%
  select(Study.ID, Cross.ID, taxa, Genus, Reciprocal, contains("Mn")) %>%
  # Gather reciprocal hybrid data
  gather(
    key = Hybrid, value = Mn.hybrid, 
    -c(Study.ID, Cross.ID, taxa, Genus, Reciprocal), -contains("Mn.sp")
    ) %>%
  drop_na(Mn.hybrid) %>%
  # Calculate standardized hybrid phenotype mean
  mutate(
    Dominance = ifelse(
      Mn.sp1 > Mn.sp2, 
      (Mn.hybrid - Mn.sp2)/abs(Mn.sp1 - Mn.sp2),
      (Mn.hybrid - Mn.sp1)/abs(Mn.sp1 - Mn.sp2)
    )
  ) %>%
  mutate_at("Cross.ID", as.factor) %>%
  arrange(taxa, Genus, Cross.ID, Hybrid) %>%
  # only get crosses with > 2 traits
  # Reciprocal hybrids are separated
  group_by(Cross.ID, Hybrid, taxa, Genus, Study.ID, Reciprocal) %>%
  filter(n() > 2) %>%
  # Create all combinations of standardized hybrid phenotype mean
  do(data.frame(t(combn(.$Dominance, 2)))) %>%
  # Calculate dominance mismatch
  mutate(
    Mismatch = sqrt(2) * sqrt( X1^2 + X2^2 - ( (X1 + X2) / sqrt(2) )^2 ),
    Mismatch.log = log(Mismatch)
  ) %>%
  left_join(meta) %>%
  mutate_if(is.character, as.factor) %>%
  within(taxa <- ordered(taxa, levels = c("Neuroptera", "Coleoptera", "Diptera", "Lepidoptera", "Orthoptera", "Aves", "Rodentia", "Anura", "Cichliformes"))) %>%
  as.data.frame

Distribution of d_mismatch

dat %>% 
  summarise(
    'Trait combination' = length(X1),
    'Species pair' = length(unique(species.pair)),
    'Crossing' = length(unique(Cross.ID))
    ) %>% 
  kable("html", caption = "Dataset summary") %>% 
  kable_styling("striped", position = "left")

Dataset summary

Trait combination	Species pair	Crossing
15107	20	27

ggplot(data = dat, aes(x = Cross.ID, y = Mismatch)) +
  geom_point(alpha = 0.4, aes(color = Hybrid)) +
  xlab("Cross ID") + ylab("dmismatch") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  facet_grid(~taxa, scales = "free") +
  ggtitle("All dmismatch values across orders")

ggplot(
  data = dat %>%
    filter(!grepl("musculus", Cross.ID)), 
  aes(x = Cross.ID, y = Mismatch)) +
  xlab("Cross ID") + ylab("dmismatch") +
  geom_point(alpha = 0.4, aes(color = Hybrid)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  facet_grid(~taxa, scales = "free") +
  ggtitle("dmismatch across orders excluding Rodentia")

d_mismatch is quite large in crossing involving mices (Rodentia), due to large trait value.

General strength of d_mismatch

Estimated mean d_mismatch across all studies by phylogenetic random model (using MCMCglmm function).

The model included following Random effects estimates:

• Study.ID: Primary studies

• sp1.name: Phylogeny of parental species

• Cross.ID: Parental strain used in the crossing. Discriminate intraspecific populations

• Hybrid: Crossing direction

• SE.units: Sampling variance of effect size

#++++++++++++++++++++++++++++++++++#
print("Phylogenetic tree for mcmcglmm")

[1] "Phylogenetic tree for mcmcglmm"

#++++++++++++++++++++++++++++++++++#

# compute branch lengths of tree
phylo_branch <- read.tree(file = "../data/phylo.novelty.tre") %>%
  compute.brlen(method = "Grafen", power = 1)
plot.phylo(phylo_branch, cex = 0.7)

# # saving phylogeneic matrix
# phylo_cor <- vcv(phylo_branch, cor = T)
# 
# # generating inverse phylogenetic matrix for MCMCglmm
# phylo_branch$node.label <- NULL
# phylo_MCMC <- MCMCglmm::inverseA(phylo_branch, nodes = "ALL", scale = TRUE)$Ainv

# phylmix <-MCMCglmm(
#   fixed = Mismatch ~  1,
#   # idh(SE):units | weight by SE of effect size
#   random = ~ Study.ID + sp1.name + Cross.ID + Hybrid,
#   verbose = FALSE,
#   ginverse = list(sp1.name = phylo_MCMC),
#   nitt = 60000,  # Increase the number of iterations, default is 13000
#   burnin = 5000,  # Increase the number of burnin, default is 3000
#   data = dat
# )
# saveRDS(phylmix, file = "../Analysis/mismatch.obj")

phylmix <- readRDS("../Analysis/mismatch.obj")

#+++++++++++++++++++++++++++#
# Fixed effects output 
#+++++++++++++++++++++++++++#
get_fixed.MCMCglmm(summary(phylmix)$solutions) %>%
  dplyr::select("Factors", "Estimates", "95% credible interval", "P", "Description") %>%
  kable("html", digits = 3, caption = "Fixed effects estimates") %>% 
  kable_styling("striped", position = "left")

Fixed effects estimates

Factors	Estimates	95% credible interval	P	Description
(Intercept)	4.41	-3.05 – 10.29	0.166	\(\beta\) = 4.41, CI = -3.05 – 10.29, P = 0.166

#+++++++++++++++++++++++++++#
# Random effects output 
#+++++++++++++++++++++++++++#
# transformation for varaince
get_random.MCMCglmm(summary(phylmix$VCV)) %>%
  kable("html", digits = 2, caption = "Random effects estimates") %>% 
  kable_styling("striped", position = "left")

Random effects estimates

Random effects	Estimates	95% credible interval
Study.ID	1.18	0–11.21
sp1.name	8.31	0–70.99
Cross.ID	42.58	13.07–101.37
Hybrid	28.14	0–61.22
units	2801.56	2740.38–2865.59

Even data included quite large d_mismatch, overall it did not differ from 0. Yet, the estimate (β = 4.41, CI = -3.05 – 10.29) is still much larger than previously reported mean d_mismatch 0.6 Thompson et al. 2019. This is presumably because novelty in phenotypic means, which should increase d_mismatch, is much more frequent in our data compared to Thompson et al. 2019.

Factors affecting d_mismatch

We asked if any species-level moderators (Genetic divergence between parents, Genet.divergence; Viability of reciprocal cross, Reciprocal; Heterogametic sex, Hetero.sex; Parents’ distribution overlap, Distribution) influence d_mismatch values by phylogenetic random model. Random effects were identical as [above] (## General pattern of mismatch d_mismatch).

dat3 <- dat %>%
  drop_na(Genet.divergence, Reciprocal, Hetero.sex, Distribution)

# phylmix <-MCMCglmm(
#   fixed = Mismatch ~  Genet.divergence + Reciprocal + Hetero.sex + Distribution,
#   # idh(SE):units | weight by SE of effect size
#   random = ~ Study.ID + sp1.name + Cross.ID + Hybrid,
#   verbose = FALSE,
#   ginverse = list(sp1.name = phylo_MCMC),
#   nitt = 60000,  # Increase the number of iterations, default is 13000
#   burnin = 5000,  # Increase the number of burnin, default is 3000
#   data = dat3
# )
# saveRDS(phylmix, file = "../Analysis/mismatch.reg.obj")

phylmix <- readRDS("../Analysis/mismatch.reg.obj")

#+++++++++++++++++++++++++++#
# Fixed effects output 
#+++++++++++++++++++++++++++#
get_fixed.MCMCglmm(summary(phylmix)$solutions)  %>%
  dplyr::select("Factors", "Estimates", "95% credible interval", "P", "Description") %>%
  kable("html", digits = 3, caption = "Fixed effects estimates") %>% 
  kable_styling("striped", position = "left")

Fixed effects estimates

Factors	Estimates	95% credible interval	P	Description
(Intercept)	-1.72	-27.5 – 23.1	0.903	\(\beta\) = -1.72, CI = -27.5 – 23.1, P = 0.903
Genet.divergence	-1.38	-11.56 – 8.6	0.788	\(\beta\) = -1.38, CI = -11.56 – 8.6, P = 0.788
ReciprocalViable	-8.27	-39.1 – 19.25	0.562	\(\beta\) = -8.27, CI = -39.1 – 19.25, P = 0.562
Hetero.sexMale	11.74	-24.15 – 50.92	0.512	\(\beta\) = 11.74, CI = -24.15 – 50.92, P = 0.512
DistributionAllopatry	-2.59	-31.14 – 27.31	0.847	\(\beta\) = -2.59, CI = -31.14 – 27.31, P = 0.847

#+++++++++++++++++++++++++++#
# Random effects output 
#+++++++++++++++++++++++++++#
# transformation for varaince
get_random.MCMCglmm(summary(phylmix$VCV)) %>%
    kable("html", digits = 2, caption = "Random effects estimates") %>% 
    kable_styling("striped", position = "left")

Random effects estimates

Random effects	Estimates	95% credible interval
Study.ID	2.54	0–23.35
sp1.name	4.78	0–48.3
Cross.ID	60.43	12.36–204.15
Hybrid	259.92	0–114.81
units	2874.72	2809.1–2941.75

None of species level moderators did not influence d_mismatch, as Thompson et al. 2019 showed.

Meta-analysis for phenotypic variation (using lnVR)

Allign parental species according to phenotypic variation

To investigate paternal effect in hybrids using difference in variance between reciprocal cross, it’s better to allign species with larger phenotypic variance (spLL_V2) and smaller CV (spSS_V2) in each traits. Name of hybrids will be changed according to change in names of parental species (hybridLS_V2 & hybridSL_V2).

#+++++++++++++++++++++++++++++++++++++++++++++++++#
# sp1 >= sp2 in phenotypic variation
#+++++++++++++++++++++++++++++++++++++++++++++++++#
# extract
sp1sp2.var <- original.dat %>%
  filter(SD.sp1 >= SD.sp2) # filtering according to CV of parentals
# change column name from sp1 or sp2 -> spL or spS
names(sp1sp2.var) <- gsub("1", "L", names(sp1sp2.var))
names(sp1sp2.var) <- gsub("2", "S", names(sp1sp2.var))

#+++++++++++++++++++++++++++++++++++++++++++++++++#
# sp1 < sp2 in phenotypic variation
#+++++++++++++++++++++++++++++++++++++++++++++++++#
# extract
sp2sp1.var <- original.dat %>%
  filter(SD.sp1 < SD.sp2)
# change column name from sp1 or sp2 -> spS or spL
names(sp2sp1.var) <- gsub("1", "S", names(sp2sp1.var))
names(sp2sp1.var) <- gsub("2", "L", names(sp2sp1.var))

#+++++++++++++++++++++++++++++++++++++++++++++++++#
# combine dataset & sort by ES.ID 
#+++++++++++++++++++++++++++++++++++++++++++++++++#
bind_rows(sp1sp2.var, sp2sp1.var) %>%
  arrange(ES.ID) %>%
  write.csv("../data/dat.var.csv", quote=F,row.names = F)

Effect size calculation

We quantified relative phenotypic variability of hybrids and the parent with large variability (spLL_V2) to the parent with small variability (spSS_V2), by using lnVR. We compared variance itself, and did not controll mean-variance relationship.
Effecct size was calculated by using escalc function in metafor package

# load spLS files
dat <- read.csv("../data/dat.var.csv", head = TRUE)

# Order by taxon
dat$taxa <- ordered(
  dat$taxa,
  levels = c("Neuroptera", "Coleoptera", "Diptera", "Lepidoptera", "Orthoptera", "Aves", "Rodentia", "Anura", "Cichliformes")
  )

var.dif <- foreach(
  cr = c("hybLS", "hybSL", "spL"), 
  .combine = `rbind`
  ) %do% {
    escalc(
      measure = "VR",
      # N of hybrids
      n1i = dat[, paste("N", cr, sep = ".")],
      # N of parentals
      n2i = dat[, "N.spS"],
      # Mean of hybrids
      m1i = dat[, paste("Mn", cr, sep = ".")],
      # Mean of parentals
      m2i = dat[, "Mn.spS"],
      # SD of hybrids]
      sd1i = dat[, paste("SD", cr, sep = ".")],
      # SD of parentals
      sd2i = dat[, "SD.spS"],
      data = dat
      ) %>%
    rename(lnVR.es = yi, lnVR.sv = vi) %>%
    mutate(cross = cr)
    } %>%
  drop_na(lnVR.es) %>%
  arrange(ES.ID) %>%
  left_join(., dat)

write.csv(var.dif, "../data/lnVR.ES.general.csv", row.names = F)

Funnel plots

res <- rma(yi = lnVR.es, vi = lnVR.sv, data = var.dif, method="FE")

## set up 2x2 array for plotting
par(mfrow=c(2,2))
## draw funnel plots
funnel(res, main="Standard Error", xlim = c(-5, 5))
funnel(res, yaxis="vi", main="Sampling Variance", xlim = c(-5, 5))
funnel(res, yaxis="seinv", main="Inverse Standard Error", xlim = c(-5, 5))
funnel(res, yaxis="vinv", main="Inverse Sampling Variance", xlim = c(-5, 5))

Jugding novelty in phenotypic variabilities

# load spLS files
dat <- read.csv("../data/dat.var.csv", head = TRUE)

#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#
# Difference between larger species (spL) and hybrids 
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#

for (i in c("spL")) { # identify type of hybrids
  for (cr in c("hybLS", "hybSL")) { # identify type of parentals
    assign(
      paste(cr, i, sep = "_"),
        escalc(
          measure = "VR",
          # N of hybrids
          n1i = dat[, paste("N", i, sep = ".")],
          # N of parentals
          n2i = dat[, paste("N", cr, sep = ".")],
          # Mean of hybrids
          m1i = dat[, paste("Mn", i, sep = ".")],
          # Mean of parentals
          m2i = dat[, paste("Mn", cr, sep = ".")],
          # SD of hybrids
          sd1i = dat[, paste("SD", i, sep = ".")],
          # SD of parentals
          sd2i = dat[, paste("SD", cr, sep = ".")],
          data = dat
          ) %>%
          select(ES.ID, yi, vi) %>%
          rename("lnVR.es" = "yi", "lnCVR.sv" = "vi") %>% 
        mutate(
          # direction of transgressve segregation - greater than parent
          direction = "+",
          # SE for logistic distribution, for the weighted binomial regression
          # SE = pi/sqrt(3*(n_e + n_c))
          SE = pi/sqrt(3*(
            dat[, paste("N", i, sep = ".")] + dat[, paste("N", cr, sep = ".")]
          ))
        )
    )
  }
}

#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#
# Difference between hybrids and smaller species (spS)
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#

for (i in c("hybLS", "hybSL")) { # identify type of hybrids
  for (cr in c("spS")) { # identify type of parentals
    assign(
      paste(i, cr, sep = "_"),
        escalc(
          measure = "VR",
          # N of hybrids
          n1i = dat[, paste("N", i, sep = ".")],
          # N of parentals
          n2i = dat[, paste("N", cr, sep = ".")],
          # Mean of hybrids
          m1i = dat[, paste("Mn", i, sep = ".")],
          # Mean of parentals
          m2i = dat[, paste("Mn", cr, sep = ".")],
          # SD of hybrids
          sd1i = dat[, paste("SD", i, sep = ".")],
          # SD of parentals
          sd2i = dat[, paste("SD", cr, sep = ".")],
          data = dat
          ) %>%
          select(ES.ID, yi, vi) %>%
          rename("lnVR.es" = "yi", "lnCVR.sv" = "vi") %>% 
        mutate(
          # direction of transgressve segregation - smaller than parent
          direction = "-",
          # SE for logistic distribution, for the weighted binomial regression
          # SE = pi/sqrt(3*(n_e + n_c))
          SE = pi/sqrt(3*(
            dat[, paste("N", i, sep = ".")] + dat[, paste("N", cr, sep = ".")]
          ))
        )
    )
  }
}


#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#
# Data output
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++#

bind_rows(
  ## Difference with mother species ###
  bind_rows(hybLS_spL, hybSL_spS) %>%
    mutate(parental = "Mother"),
  ## Difference with father species ###
  bind_rows(hybLS_spS, hybSL_spL) %>%
    mutate(parental = "Father")
  ) %>%
  drop_na(lnVR.es) %>%
  left_join(., dat) %>%
  # Assign novel variability to 1, non-novel variability to 0 for each effect size
  mutate(Novelty = ifelse(lnVR.es < 0, 1, 0)) %>%
  ## Join with metadata ###
  left_join(
    .,
    read.xlsx(
      "../data/original.data.xlsx", sheet = "Species.level.moderators"
      )
    ) %>%
  mutate_at(vars(contains("divergence")), as.numeric) %>%
  mutate_at(vars(contains("divergence")), scale) %>%
  as.data.frame %>%
  select(-trait) %>%
  drop_na(Hetero.sex, trait.type, Genet.divergence) %>%
  # order taxon
  within(
    taxa <- ordered(
      taxa,
      levels = c("Neuroptera", "Coleoptera", "Diptera", "Lepidoptera", "Orthoptera", "Aves", "Rodentia", "Anura", "Cichliformes")
      )
    ) %>%
  mutate_if(is.character, as.factor) %>%
  # Insect or no
  mutate(
    insect = ifelse(taxa %in% c("Aves", "Rodentia", "Anura", "Cichliformes"), "no", "insect"),
  ) %>%
  write.csv("../data/lnVR.ES.Novelty.csv", row.names = F)

Reload data & Prepare phylogenetic tree

# Load effect sizes
var.dif <- read.csv("../data/lnVR.ES.general.csv", head = TRUE)%>%
  # indicate dataset including novel variability
  mutate(data.type = "Alltraits")

# Observations with novel variability
All <- read.csv("../data/lnVR.ES.novelty.csv", head = TRUE) %>%
  filter(lnVR.es < 0) %>%
  distinct(ES.ID)

# Effect sizes with non-novel variability
Nonovel <- read.csv("../data/lnVR.ES.general.csv", head = TRUE) %>%
  filter(!ES.ID %in% unique(All$ES.ID)) %>%
  # indicate dataset without novel variability
  mutate(data.type = "Nonnoveltraits")

# combine dataset with/without novel variability
Novel.Nonnovel <- bind_rows(var.dif, Nonovel) %>%
  mutate(metaunit =  str_c(data.type, cross, sep = "_")) %>%
  # Use observations with both reciprocal crosses
  drop_na(contains("Mn"), contains("SD")) %>%
  # Insect or no
  mutate(
    insect = ifelse(taxa %in% c("Cichliformes", "Anura", "Aves", "Rodentia"), "no", "insect"),
  )

#+++++++++++++++++++++++++++++++#
# Phylogeny 
#+++++++++++++++++++++++++++++++#

# # matching names from open tree taxonomy
# taxa <- tnrs_match_names(
#   names = levels(var.dif$spL.name) %>%
#     str_replace_all("_", " "),
#   context_name = "Animals"
#   )
# 
# # Create tree
# tree <- tol_induced_subtree(ott_ids = taxa$ott_id)
# tree$tip.label %<>%
#   strip_ott_ids # remove OTT IDs from tip labels
# # randomly solve non-binary phylogeny
# set.seed(6)
# bin.tree <- multi2di(tree, random = T)
# 
# # Fix names of tip labels
# bin.tree$tip.label %<>% str_replace_all("Dryophytes", "Hyla")
# # Indicate mismatch between tip labels & dataset species names
# setdiff(levels(as.factor(bin.tree$tip.label)), levels(var.dif$spL.name))
# setdiff(levels(var.dif$spL.name), levels(as.factor(bin.tree$tip.label)))
# 
# write.tree(bin.tree, file= "../data/phylo.lnVR.tre")

# compute branch lengths of tree
phylo_branch <- read.tree(file = "../data/phylo.lnVR.tre") %>%
  compute.brlen(bin.tree, method = "Grafen", power = 1)

# saving phylogeneic matrix
phylo_cor <- vcv(phylo_branch, cor = T)

# Note one of the tips is called "Lates calcarifer (estimated)"
phylo_branch$node.label <- NULL

# Plot tree
plot.phylo(phylo_branch, cex = 0.7)

Dominance in phenotypic variability (coefficient of variation)

We compared midparent-value of phenotypic variability and hybrids’ phenotypic variability by calculating log ratio of phenotypic variability (lnVR) of midparent-value and hybrids to spSS_V.

It’s sampling variance was substitute by that of spLL_V

We statistically compared those effect sizes through the formal meta-regression by using rma.mv function of R package metafor

All meta-analytic models included following Random effects estimates:

• Study.ID: Primary studies. Denoted as Study

• Cross.ID: Parental strain used in the crossing. Discriminate intraspecific populations. Denoted as crossed strain

• spL.name: Phylogeny of parental species (spLL_M). Denoted as species with phylogeny

#+++++++++++++++++++++++++++++++++++++++++++#
# Calculate midparent 
#+++++++++++++++++++++++++++++++++++++++++++#
midparent <- Novel.Nonnovel %>%
   mutate(
     lnVR.midparent.es =
       ifelse(
         cross == "spL",
         # log(Se/Sc)
         log((SD.spL + SD.spS)/2) - log(SD.spS) +  
           # 1/2(Ne-1) - 1/2(Nc-1)
           1/(N.spL + N.spS -2) - 1/(2*N.spS - 2), 
         lnVR.es
         )
     # Divide variance by 4 in spL whereas hybrids are constant
     ) %>%
   mutate_at("cross", as.factor) %>%
   within(levels(cross) <- c("hybLS", "hybSL", "midparent"))


#+++++++++++++++++++++++++++++++++++++++++++++++++++#
# Compare midparent and hybrids by meta-regresson
#+++++++++++++++++++++++++++++++++++++++++++++++++++#

for (tr in c("Alltraits", "Nonnoveltraits")) {
  for (taxon in c("alltaxon", "insect")) {
    
    # Filtering taxon (all taxon or insects)
    if (taxon == "alltaxon") {
      dat <- midparent %>% 
        filter(data.type == tr)
    } else {
      dat <- midparent %>% 
        filter(data.type == tr, insect == taxon)
    }

    #+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++#
    # Estimate meta-analytic mean of midparent of phenotypic variation
    #+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++#
    # midparent.compare <- rma.mv(
    #   yi = lnVR.midparent.es, V = lnVR.sv,
    #   data = dat,
    #   method = "REML",
    #   random = list(~1 | spL.name, ~1 | Study.ID, ~1 | Cross.ID, ~1 | ES.ID),
    #   R = list(spL.name = phylo_cor),
    #   mods = ~ relevel(cross, ref = "midparent")
    #   )
    # ## Save model ###
    # saveRDS(
    #   midparent.compare,
    #   file = paste("../Analysis/lnVR.midparent.compare", tr, taxon, "obj", sep = ".")
    #   )
  
    assign(
      paste("Dominance", tr, taxon, sep = "."),
      readRDS(paste("../Analysis/lnVR.midparent.compare", tr, taxon, "obj", sep = ".")) %>% 
        get_reg() %>%
        # Show dataset name
        within(Dataset <- c(paste(tr, taxon), rep("", length(.$Estimate)-1))) %>%
        # Rename fixed effects
        within('Fixed effects' <- c("", "midparent (intrcpt)", "hybLS", "hybSL")) %>%
        # Difference of each cross from midparent in %
        mutate('Comparison with midparent' = c(
          rep("", 2),
          # Difference hybLS - midparent
          paste(
            round(
            100*(exp(midparent.compare$beta[1]+midparent.compare$beta[2])            -exp(midparent.compare$beta[1])), # hybrid LS
            2),
            "% larger"
            ),
              # beta[1] : midparent
              # beta[2] : hybLS
              # beta[3] : hybSL
          # Difference hybSL - midparent
          paste(
            round(
            100*(exp(midparent.compare$beta[1]+midparent.compare$beta[3])-exp(midparent.compare$beta[1])), # hybrid SL
            2),
            "% larger"
            )
          ))
      )

    #++++++++++++++++++++++++++++++++++++++++++++++#
    # Meta-analysis for midparent to plot band
    #++++++++++++++++++++++++++++++++++++++++++++++#
    # midparent.random <- rma.mv(
    #   yi = lnVR.midparent.es,
    #   V = lnVR.sv,
    #   data = dat %>%
    #     filter(cross == "midparent"),
    #   method = "REML",
    #   random = list(~1 | spL.name, ~1 | Study.ID, ~1 | Cross.ID, ~1 | ES.ID),
    #   R = list(spL.name = phylo_cor)
    #   )
    # ## Save model ###
    # saveRDS(
    #   midparent.random,
    #   file = paste("../Analysis/lnVR.midparent", tr, taxon, "obj", sep = ".")
    #   )
    
  }
}

bind_rows(
  Dominance.Alltraits.alltaxon, Dominance.Nonnoveltraits.alltaxon,
  Dominance.Alltraits.insect, Dominance.Nonnoveltraits.insect
  ) %>%
  kable("html", digits = 3, caption = "Meta-analyses results of full dataset and data subsets") %>%
  kable_styling("striped", position = "left") %>%
  scroll_box(width = "100%", height = "500px")

Meta-analyses results of full dataset and data subsets

Dataset	Fixed effects	Estimate	LowerCI	UpperCI	P	I2[total]	%I2[species with phylogeny]	%I2[study]	%I2[crossed strain]	%I2[residual]	significance	Comparison with midparent
Alltraits alltaxon						87.9%	1.2%	14.2%	0%	72.6%
	midparent (intrcpt)	0.542	0.340	0.743	0.000
	hybLS	-0.049	-0.116	0.019	0.157							-21.11 % larger
	hybSL	-0.083	-0.150	-0.016	0.015							-24.94 % larger
Nonnoveltraits alltaxon						82.1%	0%	23.9%	0%	58.2%
	midparent (intrcpt)	0.756	0.536	0.976	0.000
	hybLS	-0.142	-0.232	-0.052	0.002							-21.11 % larger
	hybSL	-0.140	-0.228	-0.051	0.002							-24.94 % larger
Alltraits insect						87.7%	0%	10%	0%	77.7%
	midparent (intrcpt)	0.478	0.300	0.656	0.000
	hybLS	-0.063	-0.133	0.006	0.074							-21.11 % larger
	hybSL	-0.107	-0.176	-0.038	0.002							-24.94 % larger
Nonnoveltraits insect						82.4%	0%	24.2%	0%	58.2%
	midparent (intrcpt)	0.722	0.485	0.960	0.000
	hybLS	-0.167	-0.260	-0.074	0.000							-21.11 % larger
	hybSL	-0.179	-0.270	-0.088	0.000							-24.94 % larger

Crossing direction effect in phenotypic variability (coefficient of variation)

We statistically compared lnVR from spSS_M to the other crosses through the formal meta-regression by using rma.mv function of R package metafor.
Here we asked if lnVR of hybSL_V was smaller/larger than that of hybLS_V. Smaller lnVR of hybSL_V indicates maternal inheritance in phenotypic variability

for (tr in c("Alltraits", "Nonnoveltraits")) {

  if (taxon == "alltaxon") {
    dat <- Novel.Nonnovel %>% 
      filter(data.type == tr)
  } else {
    dat <- Novel.Nonnovel %>% 
      filter(data.type == tr, insect == taxon)
  }
    
  for (taxon in c("alltaxon", "insect")) {
  #++++++++++++++++++++++++++++++++++++++++++++++#
  # phylogenetic random regresson (ANOVA)
  #++++++++++++++++++++++++++++++++++++++++++++++#
  # phyl.random.hybLS <- rma.mv(
  #   yi = lnVR.es, V = lnVR.sv,
  #   data = dat, method = "REML",
  #   random = list(~1 | spL.name, ~1 | Study.ID, ~1 | Cross.ID, ~1 | ES.ID),
  #   R = list(spL.name = phylo_cor),
  #   mods = ~ cross
  #   )
  # # Save model ###
  # saveRDS(
  #   phyl.random.hybLS,
  #   file = paste("../Analysis/lnVR.compare.from.hybLS", tr, taxon, "obj", sep = ".")
  #   )
    
    phyl.random.hybLS <- readRDS(
      paste("../Analysis/lnVR.compare.from.hybLS", tr, taxon, "obj", sep = ".")
      )

    assign(
      paste("Reciprocal", tr, taxon, sep = "."),
      phyl.random.hybLS %>% 
        get_reg() %>%
        # Show dataset name
        within(Dataset <- c(paste(tr, taxon), rep("", length(.$Estimate)-1))) %>%
        # Rename fixed effects
        within('Fixed effects' <- c("", "hybLS (intrcpt)", "hybSL", "spLL")) %>%
        # Difference of each cross from midparent in %
        mutate('Comparison with hybridLS' = c(
          rep("", 2),
          paste(
            round(
              (exp(phyl.random.hybLS$beta[1]+phyl.random.hybLS$beta[2]) -
                 exp(phyl.random.hybLS$beta[1])
               )*100,
              2),
            "% larger"
            ), 
          ""
          )
        )
      )
  }
}

bind_rows(
  Reciprocal.Alltraits.alltaxon, Reciprocal.Nonnoveltraits.alltaxon,
  Reciprocal.Alltraits.insect, Reciprocal.Nonnoveltraits.insect
  ) %>%
  kable("html", digits = 3, caption = "Meta-analyses results of full dataset and data subsets") %>% 
  kable_styling("striped", position = "left") %>%
  scroll_box(width = "100%", height = "500px")

Meta-analyses results of full dataset and data subsets

Dataset	Fixed effects	Estimate	LowerCI	UpperCI	P	I2[total]	%I2[species with phylogeny]	%I2[study]	%I2[crossed strain]	%I2[residual]	significance	Comparison with hybridLS
Alltraits alltaxon						89.6%	0%	14.2%	0%	75.3%
	hybLS (intrcpt)	0.498	0.301	0.696	0.000
	hybSL	-0.032	-0.104	0.039	0.378							-5.21 % larger
	spLL	0.280	0.212	0.347	0.000
Nonnoveltraits alltaxon						85.8%	0%	23.4%	0%	62.4%
	hybLS (intrcpt)	0.641	0.392	0.890	0.000
	hybSL	0.007	-0.088	0.101	0.893							1.24 % larger
	spLL	0.416	0.326	0.506	0.000
Alltraits insect						89.4%	0%	9.6%	0%	79.8%
	hybLS (intrcpt)	0.425	0.233	0.618	0.000
	hybSL	-0.042	-0.116	0.032	0.261							-6.35 % larger
	spLL	0.293	0.223	0.363	0.000
Nonnoveltraits insect						86.1%	0%	23.6%	0%	62.5%
	hybLS (intrcpt)	0.582	0.312	0.852	0.000
	hybSL	-0.009	-0.107	0.089	0.857							-1.6 % larger
	spLL	0.438	0.345	0.531	0.000

Session information

sessionInfo()  # show R version etc

R version 3.6.3 (2020-02-29)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19041)

Matrix products: default

locale:
[1] LC_COLLATE=Japanese_Japan.932  LC_CTYPE=Japanese_Japan.932   
[3] LC_MONETARY=Japanese_Japan.932 LC_NUMERIC=C                  
[5] LC_TIME=Japanese_Japan.932    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggimage_0.2.8      patchwork_1.1.1    ggtree_2.1.1       ggbeeswarm_0.6.0  
 [5] ggiraphExtra_0.3.0 svglite_1.2.3.2    pander_0.6.3       kableExtra_1.3.1  
 [9] knitr_1.30         phytools_0.7-70    maps_3.3.0         rotl_3.0.11       
[13] orchaRd_0.0.0.9000 MCMCglmm_2.30      ape_5.4-1          coda_0.19-4       
[17] metafor_2.4-0      Matrix_1.2-18      foreach_1.5.1      magrittr_2.0.1    
[21] forcats_0.5.0      stringr_1.4.0      dplyr_1.0.2        purrr_0.3.4       
[25] readr_1.4.0        tidyr_1.1.2        tibble_3.0.4       ggplot2_3.3.3     
[29] tidyverse_1.3.0    openxlsx_4.2.3    

loaded via a namespace (and not attached):
  [1] readxl_1.3.1            uuid_0.1-4              backports_1.2.1        
  [4] fastmatch_1.1-0         systemfonts_0.3.2       plyr_1.8.6             
  [7] igraph_1.2.6            lazyeval_0.2.2          splines_3.6.3          
 [10] mycor_0.1.1             rncl_0.8.4              digest_0.6.27          
 [13] htmltools_0.5.0         magick_2.5.2            fansi_0.4.1            
 [16] modelr_0.1.8            prettyunits_1.1.1       colorspace_2.0-0       
 [19] rvest_0.3.6             haven_2.3.1             xfun_0.20              
 [22] crayon_1.3.4            jsonlite_1.7.2          phangorn_2.5.5         
 [25] iterators_1.0.13        glue_1.4.2              gtable_0.3.0           
 [28] ppcor_1.1               webshot_0.5.2           sjmisc_2.8.6           
 [31] rentrez_1.2.3           scales_1.1.1            DBI_1.1.0              
 [34] Rcpp_1.0.5              plotrix_3.7-8           viridisLite_0.3.0      
 [37] progress_1.2.2          tmvnsim_1.0-2           gridGraphics_0.5-1     
 [40] tidytree_0.3.3          htmlwidgets_1.5.3       httr_1.4.2             
 [43] RColorBrewer_1.1-2      ellipsis_0.3.1          farver_2.0.3           
 [46] pkgconfig_2.0.3         XML_3.99-0.3            dbplyr_2.0.0           
 [49] labeling_0.4.2          ggplotify_0.0.5         tidyselect_1.1.0       
 [52] rlang_0.4.10            reshape2_1.4.4          munsell_0.5.0          
 [55] cellranger_1.1.0        tools_3.6.3             cli_2.2.0              
 [58] generics_0.1.0          pacman_0.5.1            sjlabelled_1.1.7       
 [61] broom_0.7.3             evaluate_0.14           yaml_2.2.1             
 [64] fs_1.5.0                zip_2.1.1               nlme_3.1-151           
 [67] ggiraph_0.7.8           xml2_1.3.2              compiler_3.6.3         
 [70] rstudioapi_0.13         beeswarm_0.2.3          curl_4.3               
 [73] reprex_0.3.0            treeio_1.11.2           clusterGeneration_1.3.7
 [76] stringi_1.5.3           highr_0.8               gdtools_0.2.3          
 [79] cubature_2.0.4.1        lattice_0.20-38         tensorA_0.36.2         
 [82] vctrs_0.3.6             pillar_1.4.7            lifecycle_0.2.0        
 [85] BiocManager_1.30.10     combinat_0.0-8          insight_0.11.1         
 [88] corpcor_1.6.9           R6_2.5.0                vipor_0.4.5            
 [91] codetools_0.2-16        MASS_7.3-53             gtools_3.8.2           
 [94] assertthat_0.2.1        withr_2.3.0             mnormt_2.0.2           
 [97] mgcv_1.8-31             expm_0.999-5            parallel_3.6.3         
[100] hms_0.5.3               quadprog_1.5-8          grid_3.6.3             
[103] rmarkdown_2.6           rvcheck_0.1.8           numDeriv_2016.8-1.1    
[106] scatterplot3d_0.3-41    lubridate_1.7.9.2      

References

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